Applied Science, Faculty of
Electrical and Computer Engineering, Department of
DSpace
UBCV
Wu, Wentao
Doctor of Philosophy - PhD
University of British Columbia
Multiphoton microscopy (MPM) is an optical microscopy technique visualizing tissues non-invasively in high resolution. MPM utilizes femtosecond laser pulses for multiphoton excitation. MPM provides tissue-specific contrasts that are useful in clinical applications. Two-photon excitation fluorescence (2PEF) visualizes cells; second harmonic generation (SHG) visualizes collagen fibers; third harmonic generation (THG) visualizes lipids. While being utilized in research for decades, clinical applications are still limited. The bulkiness and complexity of free-space lasers, microscope platforms, and scanners prevent convenient clinical use. Different from ex-vivo imaging where labeling can enhance signals, clinical imaging relies on label-free signals which can be significantly weaker. Multimodal MPM with multiple contrasts from two- and three-photon excitations helps clinical imaging enhance biochemical specificity and provide complementary information. Depth-scanning further provides volumetric information about tissue. A handheld MPM imaging head with multimodal and depth-scanning capabilities is needed to carry the technique to clinical applications. This dissertation explores the potential of developing compact depth-scanning multimodal MPM with simultaneous two- and three-photon imaging. Harmonic generation is theoretically analyzed under focused Gaussian beam. The impact of phase-matching is discussed to show interface-sensitivity of SHG and THG. A 1580-nm Er-doped femtosecond fiber laser serves as a compact source for excitation, and part of the laser is converted to 790 nm to achieve dual-wavelength two- and three-photon excitation. Signals of 2PEF, SHG, and THG are separated and then detected by photomultiplier tubes. A signal subtraction method is proposed to address crosstalk. To further improve the compactness, a miniaturized depth-scanning objective (numerical aperture ~0.53) consisting of an aspheric and a plano-convex lens is fabricated. A shape-memory-alloy actuator actuates the aspheric lens for depth scanning over a range of 370 μm. A denoising algorithm is developed to improve the quality of weak-signal images. The denoising method is demonstrated on weak-signal images and compared with other denoising methods, showing outperforming results. Information-rich images containing 2PEF, SHG, and THG contrasts are acquired label-free from animal, human, and plant tissues at various depths up to >200 μm. The performance is evaluated with advantages and limitations discussed. Through this study, the compact depth-scanning multimodal MPM shows great potential for clinical applications.
Development of Compact Multimodal Two-Photon and Three-Photon Microscopy by Wentao Wu B.Sc. in Electronic Science and Technology, Harbin Institute of Technology, 2015 Ph.D. in Physical Electronics, Harbin Institute of Technology, 2020 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (ELECTRICAL AND COMPUTER ENGINEERING) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) August 2024 © Wentao Wu, 2024 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Development of Compact Multimodal Two-Photon and Three-Photon Microscopy submitted by Wentao Wu in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Electrical and Computer Engineering Examining Committee: Shuo Tang, Professor, Electrical and Computer Engineering, UBC Supervisor Haishan Zeng, Professor, Dermatology and Skin Science, UBC Supervisory Committee Member Calum MacAulay, Clinical Associate Professor, Pathology and Laboratory Medicine, UBC University Examiner Boris Stoeber, Professor, Electrical and Computer Engineering, UBC University Examiner Jianan Qu, Professor, Electronic and Computer Engineering, Hong Kong University of Science and Technology External Examiner Additional Supervisory Committee Members: Robert Rohling, Professor, Electrical and Computer Engineering, UBC Supervisory Committee Member iii Abstract Multiphoton microscopy (MPM) is an optical microscopy technique visualizing tissues non-invasively in high resolution. MPM utilizes femtosecond laser pulses for multiphoton excitation. MPM provides tissue-specific contrasts that are useful in clinical applications. Two-photon excitation fluorescence (2PEF) visualizes cells; second harmonic generation (SHG) visualizes collagen fibers; third harmonic generation (THG) visualizes lipids. While being utilized in research for decades, clinical applications are still limited. The bulkiness and complexity of free-space lasers, microscope platforms, and scanners prevent convenient clinical use. Different from ex-vivo imaging where labeling can enhance signals, clinical imaging relies on label-free signals which can be significantly weaker. Multimodal MPM with multiple contrasts from two- and three-photon excitations helps clinical imaging enhance biochemical specificity and provide complementary information. Depth-scanning further provides volumetric information about tissue. A handheld MPM imaging head with multimodal and depth-scanning capabilities is needed to carry the technique to clinical applications. This dissertation explores the potential of developing compact depth-scanning multimodal MPM with simultaneous two- and three-photon imaging. Harmonic generation is theoretically analyzed under focused Gaussian beam. The impact of phase-matching is discussed to show interface-sensitivity of SHG and THG. A 1580-nm Er-doped femtosecond fiber laser serves as a compact source for excitation, and part of the laser is converted to 790 nm to achieve dual-wavelength two- and three-photon excitation. Signals of 2PEF, SHG, and THG are separated and then detected by photomultiplier tubes. A signal subtraction method is proposed to address crosstalk. iv To further improve the compactness, a miniaturized depth-scanning objective (numerical aperture ~0.53) consisting of an aspheric and a plano-convex lens is fabricated. A shape-memory-alloy actuator actuates the aspheric lens for depth scanning over a range of 370 μm. A denoising algorithm is developed to improve the quality of weak-signal images. The denoising method is demonstrated on weak-signal images and compared with other denoising methods, showing outperforming results. Information-rich images containing 2PEF, SHG, and THG contrasts are acquired label-free from animal, human, and plant tissues at various depths up to >200 μm. The performance is evaluated with advantages and limitations discussed. Through this study, the compact depth-scanning multimodal MPM shows great potential for clinical applications. v Lay Summary Multiphoton microscopy (MPM) is an optical microscopy technique that non-invasively visualizes living tissues in high resolution. While its tissue-specific contrasts are desired in clinical practice, traditional MPM systems are bulky and clinical applications are limited. Miniaturization and improvements are needed to bring the technology into clinical applications. This dissertation facilitates a handheld depth-resolved multimodal MPM system for clinical applications. In this dissertation, a compact depth-scanning MPM system with simultaneous two- and three-photon imaging is demonstrated. Theoretical analyses on harmonic generation are conducted to optimize focusing parameters and to show the interface sensitivity of harmonics. Dual excitation wavelengths provide label-free two- and three-photon excitation. The separation of multi-channel signals is discussed with an issue of crosstalk addressed. A miniaturized depth-scanning objective is fabricated. A denoising algorithm is developed to improve weak-signal images. Multimodal images are acquired from tissues. Through this study, the compact multimodal MPM shows great potential for clinical applications. vi Preface All the work presented henceforth was conducted in the Biophotonics Laboratory at the University of British Columbia (Vancouver campus). This work involved imaging of human tissues and animal tissues in its research. Approval of all ethical and experimental procedures and protocols was granted by the committees on human ethics and biosafety of the University of British Columbia under application numbers H16-02503, B17-0066, H14-03050, and SRS_BIOPER_Sep2022ang10Shu39. A version of Chapter 2 has been published in the following journal paper: 1. Wentao Wu and Shuo Tang, "Harmonic Generation and Impact of Phase Matching in Multimodal Multiphoton Microscopy". IEEE Journal of Selected Topics in Quantum Electronics 29, 7000309 (2023). I was the lead investigator, responsible for mathematical equations derivation and calculation, program writing and computation, experiment, data collection and analysis, as well as manuscript composition. Dr. Shuo Tang was the supervisory author on this project and was involved throughout the project in concept formation and manuscript composition. A version of Chapter 3 has been published in the following journal and conference papers: 2. Wentao Wu, Qihao Liu, Christoph Brandt, and Shuo Tang, "Dual-wavelength multimodal multiphoton microscope with SMA-based depth scanning". Biomedical Optics Express 13, 2754–2771 (2022). vii 3. Wentao Wu, Qihao Liu, Christoph Brandt, and Shuo Tang, "Multimodal multiphoton microscope with depth scanning". Photonics West 2022, SPIE, 11965-0J (2022). 4. Christoph Brandt, Wentao Wu, Qihao Liu, and Shuo Tang, "Endoscopic MPM objective designed for depth scanning". Photonics West 2022, SPIE, 11937-06 (2022). In the first-author journal paper and conference paper, I was the lead investigator, responsible for concept formation, software programming, experiment, data collection and analysis, as well as manuscript composition. Qihao Liu was involved in the programming and testing of the depth-scanning actuator. Christoph Brandt was involved in the optical design of the objective lens as well as computer simulation. Dr. Shuo Tang was the supervisory author on this project and was involved throughout the project in concept formation and manuscript composition. In the second-author conference paper, I was responsible for miniaturized objective engineering, assembly, and testing. A version of Chapter 4 and 5 has been published in the following journal and conference papers: 5. Wentao Wu, Christoph Brandt, Xin Zhou, and Shuo Tang, "Label-free multimodal imaging with simultaneous two-photon and three-photon microscopy and kernel-based nonlinear scaling denoising". Biomedical Optics Express 15, 114–130 (2024). 6. Wentao Wu, Christoph Brandt, and Shuo Tang, "Simultaneous 2-photon and 3-photon multimodal signal acquisition for multiphoton microscopy". Photonics West 2023, SPIE, 12384-0H (2023). 7. Wentao Wu and Shuo Tang, "Multimodal MPM Image Processing: Nonlinear Denoise Method Based on Kernel Size Determined by Tissue Feature". Biophotonics Congress: Optics in the Life Sciences (Optical Society of America 2023), NTu1C.6 (2023). viii I was the lead investigator, responsible for concept formation, experiment, data collection and analysis, software programming, Ansys simulation, as well as manuscript composition. Christoph Brandt was responsible for the optical design of the miniaturized objective as well as Zemax simulation. Xin Zhou was responsible for group collaboration during the study of the human bone sample as well as part of the image interpretation work. Dr. Shuo Tang was the supervisory author on this project and was involved throughout the project in concept formation and manuscript composition. ix Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ......................................................................................................................... ix List of Tables ............................................................................................................................... xii List of Figures ............................................................................................................................. xiii List of Abbreviations ............................................................................................................... xviii Acknowledgments ........................................................................................................................xx Dedication .................................................................................................................................. xxii Chapter 1: Introduction and Overview .......................................................................................1 1.1 Introduction of multiphoton microscopy ........................................................................ 1 1.2 Basic principles of MPM ................................................................................................ 5 1.3 Multimodal MPM with two-photon and three-photon excitations ................................. 8 1.4 Typical configuration of MPM systems ....................................................................... 10 1.5 Current clinical applications of MPM ........................................................................... 12 1.6 Development of compact MPM systems ...................................................................... 14 1.7 Depth-resolved MPM .................................................................................................... 17 1.8 Objectives and contributions ......................................................................................... 19 1.9 Outline ........................................................................................................................... 20 Chapter 2: Theory of harmonic generation and impact of phase matching in multimodal multiphoton microscopy ..............................................................................................................22 x 2.1 Introduction ................................................................................................................... 22 2.2 Analytical derivation of THG and SHG using focused Gaussian beam ....................... 25 2.3 Analyzing THG and SHG efficiencies ......................................................................... 28 2.3.1 THG and SHG in various sample thickness cases ................................................ 28 2.3.2 Optimization of the confocal parameter b ............................................................ 32 2.4 Simulation and experimental validation using layered structures ................................ 34 2.5 Demonstration of multimodal MPM imaging ............................................................... 39 2.6 Discussion and conclusion ............................................................................................ 43 Chapter 3: Depth scanning multimodal MPM realized by SMA-actuated miniaturized objective and dual excitation wavelengths .................................................................................44 3.1 Introduction ................................................................................................................... 44 3.2 Experimental setup ........................................................................................................ 48 3.3 SMA-based depth scanning .......................................................................................... 52 3.4 Characterization of field of view and resolution ........................................................... 57 3.5 Results and discussions ................................................................................................. 61 3.6 Conclusion .................................................................................................................... 65 Chapter 4: Kernel-based nonlinear scaling denoising method for low-signal MPM images 67 4.1 Introduction ................................................................................................................... 67 4.2 Denoising algorithm ...................................................................................................... 69 4.3 Verification of the denoising method ........................................................................... 71 4.4 Conclusion .................................................................................................................... 78 Chapter 5: Simultaneous two-photon and three-photon microscopy for label-free multimodal MPM imaging ..........................................................................................................80 xi 5.1 Introduction ................................................................................................................... 80 5.2 System setup ................................................................................................................. 84 5.3 Simultaneous acquisition of 2PM and 3PM signals ..................................................... 86 5.4 Combined 2PM and 3PM imaging of biological samples ............................................ 91 5.5 Discussion and conclusions .......................................................................................... 93 Chapter 6: Summary and future work ......................................................................................96 6.1 Summary ....................................................................................................................... 96 6.2 Future work ................................................................................................................... 99 Bibliography ...............................................................................................................................102 xii List of Tables Table 1.1 Comparison of SHG, 2PEF, and THG processes for MPM imaging. ............................ 8 Table 1.2 Requirement and imaging capability for 2PEF, SHG, and THG. ................................... 9 Table 3.1 Performance characterization of the SMA Actuator. .................................................... 56 Table 3.2 Measured FOV and resolution of the MPM system. .................................................... 59 xiii List of Figures Figure 1.1 Typical setup of an MPM system. DBS – dichroic beam splitter; PMT – photomultiplier tube. ..................................................................................................................... 11 Figure 2.1 Illustration of the Gaussian pumping beam and the relative position of the nonlinear medium. ........................................................................................................................................ 26 Figure 2.2 Dependence of factor |J| on the normalized phase-mismatch parameter bΔk, under conditions (a) bulk material (tight focusing limit) (z0 = −∞, z = +∞), (b) focusing on an interface (z0 = 0, z = +∞), (c) focusing inside a thin slab (z0 = −L/2, z = +L/2), and (d) focusing on the surface of a thin slab (z0 = 0, z = +L). .......................................................................................... 30 Figure 2.3 Dependence of harmonic power factor P′ on normalized confocal parameter b/L under various sample conditions. ............................................................................................................ 33 Figure 2.4 Variation of THG intensity as the focal plane scans through a glass-water-glass layered structure. (a) Simulated result of P3/P13. (b) Experimentally measured THG intensity. . 35 Figure 2.5 SHG from a SiO2-Si-SiO2 layered structure under different confocal parameters. (a) Simulated result of P2/P12. (b) Experimentally measured SHG intensity. (c) Calculated harmonic power factor P′ versus confocal parameter b under L = 4.72 μm. ................................................ 38 Figure 2.6 Two- and three-photon MPM imaging of photonic chip. (a) Measured laser fundamental and frequency-doubled spectra for THG and SHG excitation, respectively. (b) Spectra of SHG and 2PEF excited by 790 nm and THG excited by 1580 nm. (c) SHG/THG/2PEF image of photonic chip. Three regions: (1) grating coupler; (2) adiabatic taper; (3) photonic nanowire. The magenta area is an overlap of SHG and 2PEF. Scale bar is 20 μm. 40 xiv Figure 2.7 Multimodal MPM images of (a) human tooth and (b) mouse femur bone at different depths. Red – 2PEF; blue – SHG; green – THG. Scale bar is 40 μm. .......................................... 41 Figure 3.1 Setup of the multimodal MPM imaging system. L – lens; PPLN – periodically poled MgO:LiNbO3; F – filter; MEMS – microelectromechanical system; OBJ – objective lens pack; MMF – multimode fiber; DBS – dichroic beam splitter; PMT – photomultiplier tube; HV – high voltage; DAQ – data acquisition; PC – personal computer; SMA – shape memory alloy; PCIe – peripheral component interconnect express; I2C – inter-integrated circuit. ................................. 49 Figure 3.2 Design of the objective. (a) Optical layout; (b) Zemax simulation of RMS wavefront error vs. field of view. BM – laser beam; AL – aspheric lens; AR – air; CL – plano-convex lens; WT – water (water is used as a sample in simulation); DL – diffraction-limited RMS wavefront error. .............................................................................................................................................. 50 Figure 3.3 Relative irradiance (Strehl ratio) and relative harmonic intensity vs. field of view for the objective. (a) 790-nm excitation wavelength and SHG; (b) 1580-nm excitation wavelength and THG. ....................................................................................................................................... 50 Figure 3.4 Construction of the SMA-actuated objective lens pack. AC – actuator; AD – actuator driving component; LH – lens holder; AL – aspheric lens; CL – plano-convex lens; OC – objective chassis; EP – epoxy; double-headed arrows indicate moving parts and directions. ..... 53 Figure 3.5 Performance characterization of the SMA actuator. (a) LDV measurement of the actuator movement in the full range of 370 μm; (b) LDV measurement of the actuator movement with a large step size of 200 μm and a long cycle duration of 30 s; (c) Actuator position measured by Hall sensor vs. command position under closed-loop control. ................................ 55 Figure 3.6 Actuator response time measured with the lens mounted. (a) Actuating forward; (b) Actuating backward. ..................................................................................................................... 56 xv Figure 3.7 Experimentally measured FWHM resolution of the MPM system. (a) 2PEF resolution in the XY plane; (b) 2PEF resolution in the Z (depth) direction; (c) THG resolution in the XY plane; (d) THG resolution in the Z (depth) direction. ................................................................... 59 Figure 3.8 Multimodal MPM images from animal tissues at different depths. (a)–(c) Mouse ear pinna; (d) fish skin; (e) and (f) mouse femur. THG images are averaged over three frames; 2PEF and SHG images are not averaged. Red – 2PEF; Green – SHG; Blue – THG. Scale bar is 50 μm........................................................................................................................................................ 63 Figure 3.9 Multimodal MPM images from fungus and plant tissue samples at different depths. (a) Lamella (grill) of Amanita muscaria (fly agaric) mushroom; (b) lamina of Acer rubrum (red maple) leaf; (c) leaf midrib. All 2PEF, SHG, and THG images are not averaged. Red – 2PEF; Green – SHG; Blue – THG. Scale bar is 50 μm. .......................................................................... 65 Figure 4.1 Comparison of MPM images before and after denoising by outlier removal, Gaussian filter, BVLS deconvolution and KNS. SNR is calculated and shown in each image. σ – radius of the Gaussian filter; N – numbers of iteration. Cyan squares on KNS denoised images indicate the size of the kernel. Scale bar is 40 μm. .......................................................................................... 73 Figure 4.2 Comparison between KNS and N2V in processing low-signal noisy images. The N2V models used in (a) and (b) are trained by (a1) a human bone 2PEF image with a relatively high SNR and (b1) a mouse pinna THG image with an ultra-low SNR, respectively. (a2) and (b2) show the training and validation loss through the training of these two models, respectively. .... 75 Figure 4.3 Comparison of PSNR and SSIM obtained by KNS and N2V. (a) Ground truth (reference image). (b) Noisy input image created by adding Gaussian noise with a standard deviation of 35 onto (a). (c) KNS (iteration N = 2) denoised result. (d) Denoised result by an xvi N2V model trained by a relatively high SNR image (shown in Figure 4.2(a)). (e) Denoised result by an N2V model trained by the noisy input image (b) with relatively low SNR. ....................... 77 Figure 4.4 Comparison of merging multimodal MPM images with and without KNS denoising on the low-signal THG channel. The gray and blue arrows indicate examples of noise and tissue features, respectively. .................................................................................................................... 78 Figure 5.1 Optical layout of the compact multimodal MPM imaging system with the illustration of the spectral relationship of the excitation and emission signals. L – lens; PPLN – periodically poled MgO:LiNbO3; MEMS – micro-electromechanical system; OBJ – objective lens pack; MMF – multimode fiber; DBS – dichroic beam splitter; PMT – photomultiplier tube; λe – excitation wavelength. .................................................................................................................. 85 Figure 5.2 Acquisition and verification of the multimodal MPM with simultaneous 2PM and 3PM imaging. (a) Focal shift between the two excitation wavelengths; (b) 2PM and 3PM signal spectra; (c) process for crosstalk removal and adjusting the depths between 2PM and 3PM stacks. The merged image is the superposition of single-channel images with the blue, red, and green marks, respectively. THG image is denoised before merging. Red – 2PEF; blue – SHG; green – THG. Scale bar is 40 μm. ................................................................................................. 89 Figure 5.3 Motion artifacts comparison between (a) sequential and (b) simultaneous acquisition of 2PM and 3PM images of a mouse femur. ................................................................................ 90 Figure 5.4 Configuration of thermal analysis and obtained temperature distribution of single vs. dual focal-plane imaging under the same total heat power. (a) and (b) are for single focal-plane imaging; (c) and (d) are for dual focal-plane imaging. The total heat power is 4 mW and the environment temperature is set to be 22 °C. ................................................................................. 91 xvii Figure 5.5 Simultaneous 2PM and 3PM imaging of biological samples. Red – 2PEF; blue – SHG; green – THG. Scale bar is 40 μm. ....................................................................................... 93 xviii List of Abbreviations 2D Two dimensional 2PEF Two-photon excitation fluorescence 2PM Two-photon microscopy 3D Three dimensional 3PEF Three-photon excitation fluorescence 3PM Three-photon microscopy BVLS Bounded-variable least squares CARE Content-aware image restoration CARS Coherent anti-Stokes Raman scattering CLSM Confocal laser scanning microscopy CNN Convolutional neural network DAQ Data acquisition DBS Dichroic beam splitter EMCCD Electron-multiplying charge-coupled device ETL Electrically tunable lens Er Erbium FAD Flavin adenine dinucleotide FOV Field of view FWHM Full-width half-maximum GRIN Gradient-index H&E Hematoxylin and eosin xix KNS Kernel-based nonlinear scaling LDV Laser Doppler vibrometer MEMS Micro-electro-mechanical system MMF Multimode fiber MPM Multiphoton microscopy N2V Noise2Void NA Numerical aperture NADH Nicotinamide adenine dinucleotide hydrogen NIR Near-infrared OCT Optical coherence tomography PBS Polarization beam splitter PSNR Peak signal-to-noise ratio PMT Photomultiplier tube PPLN Periodically poled MgO:LiNbO3 PSF Point spread function RMS Root mean square RMSD Root-mean-square deviation SHG Second harmonic generation SMA Shape memory alloy SNR Signal-to-noise ratio SSIM Structural similarity index THG Third harmonic generation Yb Ytterbium xx Acknowledgments I offer my enduring gratitude to my supervisor Prof. Shuo Tang for giving me this opportunity to study at the Biophotonics Laboratory at the Department of Electrical and Computer Engineering, University of British Columbia. I appreciate her broad knowledge, passion, and constant patience in providing guidance during my Ph.D. study. She has enlarged my vision of science and provided coherent answers to my questions. I appreciate my supervisory committee members Prof. Robert Rohling and Prof. Haishan Zeng for their advice and suggestions on this dissertation as well as during my Ph.D. study. Special thanks to Christoph Brandt for his contribution to the objective design. Special Thanks to Qihao Liu for his contribution to SMA Arduino programming. Special thanks to Dr. Felipe Eltit, Dr. Michael Cox, and Dr. Xin Zhou for providing the human bone sample used in this study. Thanks to Dr. Myeong-Jin Ju and Dr. Jianhua Zhao for their help in the MPM project. I would also like to thank the Centre for Disease Modeling, Centre for Comparative Medicine, and Animal Care Services at the University of British Columbia for providing the mouse tissue samples. In addition, I would like to thank all members (past and present) of the Biophotonics Laboratory at UBC for their time and collaboration. The following listing order is based on the time sequence that I met with each of you. They are: Christoph Brandt, Qihao Liu, Xin Zhou, Sina Maloufi, Mitra Mirsalehi, Michael Frew, Rex Wang. xxi I also extend my thanks and gratitude to all my friends for your genuine help and company. And most importantly, I wish to thank my loving and supportive wife, Yue (Ashley) Mao, who has warmed my heart and brought hope into my life. Finally, last but by no means least, special thanks are owed to my parents, Xuezhu Kou and Jianren Wu, who have given me endless and unconditional emotional support and great financial assistance. Many of my achievements, academic or non-academic, cannot be achieved without the support from them. xxii Dedication To my mother, Xuezhu, who gives me unconditional love and support. To my father, Jianren, who captivated my childhood mind with science. To nature, truth, and freedom. 1 Chapter 1: Introduction and Overview 1.1 Introduction of multiphoton microscopy Since the advent of optical microscopes in the 16th to 17th centuries, the visualization of microstructures in tissues has resulted in significant progress in human understanding of biology, pathology, and medicine. Various types of optical microscopes such as the phase contrast microscope and fluorescence microscope were proposed following the original bright field microscope to enhance contrast in tissue imaging. After the invention of the laser, confocal laser scanning microscopy (CLSM) was demonstrated with the advantages of optical sectioning and enhanced resolution compared with widefield microscopes. However, the image penetration, source of contrast, and capability of optical sectioning are still limited due to its linear (single-photon) excitation scheme. With the advancement in ultrafast laser technologies, multiphoton microscopy (MPM) began to emerge in the 1990s [1,2] which brought optical microscopic imaging into a new era. MPM is an advanced optical microscopy technique that utilizes multiphoton excitation of fluorescence and/or nonlinear scattering signals to non-invasively visualize living tissues in high resolution with relatively deep penetration [1–6]. The nonlinear excitation offers unique advantages for MPM imaging of scattering biological tissues. First, effective depth sectioning and depth-resolved imaging can be achieved as nonlinear optical effects require high excitation photon density, and hence the signal can only be generated from the focal volume of the excitation beam. Second, the nonlinear excitation enables the use of near-infrared (NIR) excitation which significantly reduces phototoxicity and improves penetration in scattering tissues. Moreover, as the excitation and emission wavelengths are far apart, the signal can be easily separated from the 2 excitation light by dichroic mirrors and bandpass filters, which brings about low background noise and high signal-to-noise ratio (SNR). In addition, MPM can provide unique and intrinsic contrasts from various nonlinear optical effects such as second harmonic generation (SHG), two-photon excitation fluorescence (2PEF), and third harmonic generation (THG) to reveal different chemicals and structures in biological tissue. With such advantages, MPM allows label-free, high-resolution imaging of living tissues. Compared with other optical imaging modalities such as optical coherence tomography (OCT) and CLSM, MPM has unique and irreplaceable advantages. While good in penetration and imaging speed, OCT typically has a relatively low resolution of ~10 μm [7,8]. OCT and reflectance CLSM images are primarily limited to structural information and lack contrast for specific cellular or molecular features [9–12]. The fluorescence CLSM allows for imaging specific molecules, however, it typically requires staining for fluorescence [13,14]. In comparison, MPM can achieve a resolution in the sub-micron range and make use of intrinsic fluorescence from tissue. The abundant sources of contrasts in multimodal MPM (imaging with multiple modalities/contrasts, e.g. 2PEF, SHG, and THG) can provide both biochemically and structurally specific signals label-free. Multimodal MPM makes up the limitations of traditional microscopy where the signals primarily originate from attenuation (e.g., bright field microscope) or scattering (e.g., reflectance CSLM). With the first biological SHG image reported by Freund Isaac et al. in 1986 [15] followed by the first 2PEF microscopy demonstrated by Winfried Denk et al. in 1990 [1], over the past decades, MPM has become an important technique for non-invasive and label-free imaging of biological samples and has been utilized in various medical fields including cancer research, skin imaging, neurology, stomatology, cardiovascular disease diagnoses, and chronic disease 3 diagnoses [2,5,6,16–30]. In cancer research, for example, MPM has been shown to reveal structural, morphological, and fluorescence spectral changes in cancerous tissues, as well as tumor cell arrest, tumor cell-platelet interaction, tumor cell-leukocyte interaction, and metastatic colonization processes [31,32]. Skin cancer is one of the fields in which depth-resolved multimodal MPM shows great potential [16,20,33–36]. Imaging of tissue intrinsic fluorescence and collagen by multimodal MPM can effectively distinguish between normal, precancerous, and cancerous epithelial tissues [16,37,38]. Depth-scanning MPM is utilized to study blood vessels, dermal matrix composition, and depth-dependent keratinocyte metabolism in skin tissues [39–41]. Combined SHG and THG microscopy are utilized for precise mapping of biological components and non-invasive skin cancer characterization [42]. In Ref. [42], the cellular cytoplasm of keratinocytes, melanocytes, red blood cells, and fibroblasts are identified by THG, and collagen fibers are identified by SHG. However, as a two- and three-photon multimodal MPM, it lacks the autofluorescence imaging capability due to its relatively long excitation wavelength. While there are other commonly used techniques for skin cancer detection, the advantages of MPM can potentially improve the accuracy and patient experience during the diagnosis process. The traditional gold standard is biopsy, which is accurate but invasive, time-consuming, and cannot be used to screen large areas of skin [43,44]. Visual examination (naked eye or dermatoscopy) is a quick and non-invasive method typically used before resorting to biopsy, however, it has limited accuracy that can result in false positives or negatives [45,46]. Moreover, such methods typically cannot examine deeper lesions underneath the skin surface. Dermatoscopes are used to inspect skin by surface reflections and the magnification is typically limited to ×20 which cannot show cellular or microscopic morphological changes. While some variations reach up to ×400 magnification [47], it is still limited to skin surface examination and lack of tissue-specific 4 contrasts. Reflectance CLSM is another non-invasive technique utilized in skin cancer detection [48,49]. It resolves microscopic structures to provide possible early-stage melanoma detection. However, compared with MPM, it lacks tissue-specific contrasts and typically has lower penetration and higher background noise [50,51]. As a non-invasive technique as well, MPM provides multiple tissue-specific contrasts in micron-to-sub-micron resolution that can potentially improve the detection accuracy of early-stage melanoma. The long-wavelength used by MPM brings about deeper penetration up to several hundred microns. In addition, the nonlinear excitation provides inherent depth-sectioning and a significantly low background in imaging. All these advantages make MPM a promising approach to improve skin cancer detection. Other than skin cancer, 2PEF and SHG are utilized in histological diagnosis of gastric cancer without staining of the tissue, in which cancerous cellular characteristics such as irregular size and shape, enlarged nuclei, and increased nuclear-to-cytoplasmic ratio are clearly revealed [30]. In the ex-vivo MPM imaging of human gastric cancer specimens in Ref. [30], cancerous tissue shows cellular and nuclear pleomorphism. The nest of cancer cells and the surrounding fibrous stroma are revealed. Compared with healthy tissue, the intercellular space of cancerous tissue is not readily discerned, cell nuclei are enlarged and more heterogenous in size, and the nuclear-to-cytoplasmic ratio becomes greater. MPM also shows promising results in detecting oral malignancy by imaging cells and extracellular matrix in oral tissue [25,26]. In Ref. [26], progressive loss of collagen and variably reduced interstitial matrix structure and density are detected by in-vivo MPM imaging of hamster cheek pouch dysplasia and carcinoma. In addition, Multimodal MPM is also reported to be able to distinguish cancerous abnormalities in the liver, lung, bladder, colon, etc. [27–29,31]. Apart from medical research applications, MPM is also utilized in studying the microstructure, biochemistry, and metabolism of plants, animals, and other living forms [52–58]. 5 1.2 Basic principles of MPM The basic principle and imaging contrast mechanisms of MPM are based on nonlinear effects such as 2PEF, SHG, and THG. In general, the linear and nonlinear responses of materials interacting with lasers can be described as [59] , (1.1) where P is the induced dielectric polarization density, E represents the electric field, χ(1) is the linear susceptibility, and χ(n) (for n = 2, 3, ...) are the nth-order nonlinear susceptibilities of the medium. The first-order linear term includes the reflection and absorption of light, the 2nd-order term contributes to SHG, and the 3rd-order term contributes to 2PEF and THG. 2PEF is a commonly utilized nonlinear effect. The generation of 2PEF involves a quasi-simultaneous (within 10-15 ~ 10-18 s) absorption of two photons by the nonlinear material. The corresponding fluorophore molecule inside the nonlinear material is excited to a higher energy level after the two-photon absorption, and a single photon with energy slightly less than the sum of the two incident photons is emitted during relaxation from the excited state to the ground state. Biological tissues have various intrinsic fluorophores such as nicotinamide adenine dinucleotide hydrogen (NADH), flavin adenine dinucleotide (FAD), elastin, and melanin that can generate 2PEF signals [2,6]. Cellular structures can be revealed by 2PEF imaging since NADH commonly exists in the cell cytoplasm. Three-photon-excitation-fluorescence (3PEF) involves the quasi-simultaneous absorption of three photons and the emission of one fluorescent photon. In multiphoton fluorescence, the relationship between time-averaged fluorescence photon flux F(n) and average incident power Pave under n-photon excitation is given by [60] , (1.2) ( ) ( ) ( )1 2 31 2 3 ...P E E Ec c c= + + +( ) 2 4p( ) ave01 3 2 3p p1( ) 8n n nn nnn n ng a NA pF Cnn ffhst p l-- - -= 6 where n is the number of photons absorbed, λ is the wavelength of the excitation laser, τp is the laser pulse duration, fp is the repetition rate of the laser pulse, is the system collection efficiency, η is the fluorescence quantum efficiency, σn is the n-photon absorption cross-section, C is the concentration of the fluorophore, n0 is the refractive index of the medium, NA is the numerical aperture (NA) of the focusing objective, gp(n) is the nth-order temporal coherence of the excitation source, and the coefficients a2 = 64, a3 = 28.1 [60]. The relationship between power P and photon flux F is given by , (1.3) where h is the Planck constant and c is the speed of light. Therefore, for 2PEF, Eq. (1.2) becomes . (1.4) For 3PEF, Eq. (1.2) becomes . (1.5) SHG is another commonly utilized nonlinear effect for MPM. In the process of SHG, two lower-energy photons with the same wavelength interact with nonlinear materials through nonlinear scattering, and a higher-energy photon is generated with doubled frequency (halved wavelength). The relationship between SHG power and the average incident power can be described as [61,62] , (1.6) fhcP Fl=2ave2PEF 2 2p p1PPfst lµ3 2ave3PEF 3 2 2 4p pP NAPfst lµ( )( )22 2SHG avep p1P Pfctµ 7 where χ(2) is the second-order nonlinear susceptibility. In general, efficient SHG happens in materials with a noncentrosymmetric molecular organization [63], such as collagen fibers in animal and human tissues. Therefore, SHG is widely used in MPM to image tissue samples including cartilage, bone, tendon, skin, and cornea [6,64]. In THG, three lower-energy photons with the same wavelength interact with nonlinear materials through nonlinear scattering, and a higher-energy photon is generated with tripled frequency. THG is a third-order nonlinear effect and hence has a lower susceptibility compared with SHG. THG requires a medium that is not uniform in either the refractive index or the nonlinear susceptibility [65]. Thus, THG detects interfaces and inhomogeneities and adds complementary information, such as aqueous-lipidic interfaces and air-aqueous interfaces, to 2PEF and SHG images [65,66]. Therefore, THG imaging is expected to be a novel and powerful tool in multimodal MPM. The relationship between THG power and incident power can be described as [62,65,67] , (1.7) where χ(3) is the third-order nonlinear susceptibility. Eq. (1.6) and Eq. (1.7) for SHG and THG do not show the portion that contains parameters related to the focusing of the excitation beam. SHG would have a similar dependence on the focusing parameters as 2PEF, and THG would be similar to 3PEF. Therefore, THG is more sensitive to laser parameters and focusing parameters than SHG and 2PEF. The energy diagrams and comparison of SHG, 2PEF, and THG processes are summarized in Table 1.1. ( )( )23 3THG ave 2 2p p1P Pfctµ 8 Table 1.1 Comparison of SHG, 2PEF, and THG processes for MPM imaging. SHG 2PEF THG Generation process Nonlinear susceptibility 𝜒(") 𝜒($) 𝜒($) Features • No absorption of light. • Imaging noncentrosymmetric molecules such as collagen. • Spatial localization of fluorophores. • Fluorescence from intrinsic fluorophores such as NADH, FAD, elastin, and melanin. • No absorption of light. • Imaging interfaces and inhomogeneities and reveal tissue components such as lipid. 1.3 Multimodal MPM with two-photon and three-photon excitations Compared with ex-vivo imaging in research labs where sample labeling (e.g. fluorescence labeling or genetically modified fluorescence proteins) can be used to enhance the image contrast and biochemical specificity, clinical in-vivo imaging typically requires the image to be acquired label-free due to concerns about the toxicity of labeling. Label-free imaging relies on the acquisition of intrinsic tissue signals, such as autofluorescence, which can be significantly lower than labeled signals. Multimodal MPM is a powerful tool especially suitable for label-free imaging due to its capability to acquire multiple nonlinear contrasts simultaneously from tissues, enhancing the biochemical specificity. The typical nonlinear processes used in MPM imaging include SHG and 2PEF in two-photon excitation, and THG and 3PEF in three-photon excitation. Because the different contrasts originate from different nonlinear effects that are sensitive to different tissue structures and/or chemical components, multimodal MPM imaging can reveal complementary information about tissues. 2PEF and 3PEF are both fluorescence and typically reveal the same chemical components 9 such as NADH, FAD, and elastin. Depending on the excitation wavelength, either 2PEF or 3PEF can be used to acquire the intrinsic fluorescence signals from tissues. For example, Fluorescence can show cellular structures in skin imaging. SHG and THG are nonlinear scattering and they are both sensitive to phase matching and interfaces. Phase matching describes the phase relationship between the interacting waves [68,69], and a detailed mathematical expression will be provided in Chapter 2. As a χ(2) effect, SHG further requires noncentrosymmetric molecular structures and specifically reveals collagen in tissues. On the other hand, THG is a χ(3) effect that does not require noncentrosymmetric molecular structures. THG can be used to image interfaces in tissues, such as the lipid-aquatic interface, which normally cannot be imaged by 2PEF or SHG. Therefore, multimodal MPM that combines both two-photon and three-photon excitation with label-free 2PEF, SHG, and THG imaging can show comprehensive and complementary information about tissue and be useful in clinical applications. Table 1.2 shows the requirement and imaging capability for 2PEF, SHG, and THG. Table 1.2 Requirement and imaging capability for 2PEF, SHG, and THG. Requirements 2PEF SHG THG Fluorophore Yes No No Phase matching No Yes Yes Interfaces No Yes Yes Noncentrosymmetric molecules No Yes No Most common MPM systems use two-photon imaging because two-photon excitation has higher efficiency and thus, higher signal intensity, than three-photon excitation [62,70,71]. With two-photon imaging (2PEF and SHG), structures such as cells, collagen, and elastin can be imaged. 10 Some MPM systems are dedicated to three-photon imaging for specific research applications, such as neuron science, for deeper penetration depth enabled by the longer excitation wavelength [65,72,73]. With THG imaging, biocomponents such as lipids can be revealed. For label-free imaging, few MPM systems support both two- and three-photon imaging due to the limitation of excitation wavelengths, imaging optics, and signal acquisition. Intrinsic fluorescence requires an excitation wavelength of <1 μm, however, to keep the signal from the ultraviolet range, an excitation wavelength of >1 μm is needed for THG. Thus, normally two- and three-photon imaging requires two different excitation wavelengths. Furthermore, when using two excitation wavelengths, the THG signal can overlap with 2PEF and cause challenges in signal separation and acquisition. Therefore, multimodal MPM is typically more complex, and it is difficult to acquire two- and three-photon contrasts simultaneously. This dissertation facilitates the combined label-free two- and three-photon multi-contrast imaging. A more detailed literature review and discussion on combining two-photon and three-photon microscopy will be provided in Chapters 3 and 5. 1.4 Typical configuration of MPM systems Figure 1.1 shows a typical layout of an MPM system, which contains a pulsed laser source, a scanning mechanism, a pair of scan lens and tube lens, an objective, a dichroic beam splitter (DBS) to separate the signal from the excitation beam, and a signal detector which typically contains photomultiplier tubes (PMTs). Femtosecond lasers are typically utilized due to the high peak power required to efficiently generate multiphoton nonlinear effects. Most previously reported MPM systems used femtosecond Ti:Sapphire lasers from which pulses as short as 10 fs can be achieved [74]. Erbium (Er) doped femtosecond fiber lasers with a typical pulsewidth of ~100 fs 11 are also utilized for compact and cost-effective system designs. The excitation laser is normally in the NIR range, and the excited multiphoton signals typically lie in the visible range. The scanning mechanism performs a raster scanning of the excitation beam. The excitation beam passes the scan lens and tube lens to be expanded and collimated to fill the back aperture of the objective. The objective focuses the excitation beam onto the sample. For a backward detection scheme, the signals generated from the focal volume are collected backward by the objective and then separated by a dichroic beam splitter from the excitation beam. Because multiphoton-excited signals are typically very weak, photomultiplier tubes are normally required to detect the signals. Controlled by the scanning mechanism, the excitation beam shines on one pixel for a short period of time (pixel dwell time, typically in the μs range), and the PMT detects the emitted photons which determines the intensity of that pixel on the image. By raster scanning the laser beam in the XY plane, a 2D image can be formed. Depth scanning of the focal plane in the Z direction can be added to the 2D scanning to obtain image stacks from a volume. Figure 1.1 Typical setup of an MPM system. DBS – dichroic beam splitter; PMT – photomultiplier tube. 12 1.5 Current clinical applications of MPM While it has been more than three decades since the demonstration of the first two-photon microscope, most applications of MPM are still within the field of research, imaging ex-vivo tissue samples or in-vivo imaging of small animals. Multiple challenges have limited the current clinical application of MPM imaging, including the bulky size and complexity of the system, high costs, laser safety, and imaging quality. The traditional microscope platform and commonly used free-space Ti:Sapphire lasers bring about disadvantages in size and complexity and requirements for precise alignment. For clinical uses, the laser safety requirement limits the excitation power and can lead to insufficient SNR. Especially for label-free imaging, which is commonly required in clinical uses, the signal can further decrease. As tissue is often highly scattering, the penetration depth is also limited in non-invasive clinical imaging. One current clinical application of MPM technology is skin imaging. B. W. Graf et al. demonstrated a combined MPM and optical coherence microscopy (OCM) for in-vivo human skin imaging [75]. However, the system is still based on a microscope platform with a bulky free-space Ti:Sapphire laser, galvo scanners, and traditional microscope objective. The MPM imaging contrast is limited to 2PEF only, and the depth scanning is based on moving the sample (body part) using a specially designed imaging mount. JenLab GmbH (Germany) has commercialized a two-photon microscope for skin imaging [76]. The laser source, scanning mechanisms, and control units are packaged onto a cart where the imaging optics are mounted into an articulated arm for patient imaging [76,77]. Using the MPTflex system from Jenlab, M. Balu et al. demonstrated in-vivo two-photon human skin imaging for the diagnosis of basal cell carcinoma [36]. With MPTflex, G. Lentsch et al. demonstrated in-vivo two-photon imaging of morphological features in melasma lesions and adjacent normal skin in 12 patients [78]. Also using MPTflex, V. Elagin et 13 al. performed in-vivo two-photon imaging of dermoscopic equivocal melanocytic skin lesions in human patients [79]. Nevertheless, the MPTflex system can only perform two-photon imaging and the use of an articulated arm still limits the access to certain body sites. Its imaging speed is 6 seconds per frame for 512 × 512 pixels (and can be slower for larger pixels) [36,80], which is slow and susceptible to motion artifacts for in-vivo imaging. It is also challenging for the imaging penetration depth of ~100 μm to meet the needs for clinical uses [36,78]. In addition, the 250-kg system still relies on relatively bulky and expensive Ti:Sapphire lasers, galvo scanners, and piezo-driven optics, and the 200-fs laser pulsewidth is relatively long for high-efficient MPM excitations [80]. Apart from the Jenlab system, H. Wang et al. demonstrated two-photon in-vivo human skin imaging with video frame rate by using a resonance scanner [81]. The system also has an articulated imaging arm which enables patient imaging. However, the system is still based on a bulky free-space Ti:Sapphire laser and microscope platform. The MPM system is two-photon only and has no depth-scanning. A. Fast et al. demonstrated an MPM system with an articulated imaging arm for in-vivo human skin imaging [82]. This system is made further compact by employing an Er-doped femtosecond fiber laser. However, this system is still limited to two-photon imaging. The two galvo scanners and the piezoelectric depth scanner as well as the microscope objective are relatively bulky, which increases the size of the imaging head. There are other studies in which in-vivo MPM imaging is performed on patients [83–86], however, they are primarily based on bulky lasers, scanners, and objectives, lack miniaturized scanning mechanisms, and are limited to two-photon imaging. In an articulated arm design, the beam is delivered by free-space optics such as mirrors mounted inside the articulated arm. Unlike fiber delivering, the flexibility of the imaging unit is limited by the few joints in the articulated arm. Those limitations still primarily prevent the large-scale and convenient clinical use of MPM. Therefore, there is a need to develop fiber- 14 delivered handheld-type MPM systems that would bring about a more competitive potential in clinical applications. While still under development, there are efforts done to develop MPM systems with a handheld imaging head which will be reviewed in the next section. 1.6 Development of compact MPM systems Traditional MPM imaging systems are mostly built with free-space lasers on bulky microscope platforms which limit the clinical applications. New advancements in the miniaturization of MPM systems are needed and being developed to expand the potential of MPM imaging. G. Liu et al. demonstrated a compact MPM system based on a ytterbium-doped femtosecond fiber laser [87]. The imaging optics are packaged into a handheld imaging probe. A customized objective with an NA of 0.65 and a diameter of 15.3 mm is utilized to realize the compactness. However, the system has no depth scanning and is limited to two-photon imaging. The system uses two galvo scanners for planar scanning which still has room for improvement in miniaturization. A. Dilipkumar et al. demonstrated a two-photon imaging system based on a frequency-doubled Er-doped femtosecond fiber laser and compact imaging optics which can be packaged as a handheld probe [5]. A gradient index (GRIN) objective is utilized together with a coupling objective to miniaturize the imaging head. However, the system is limited to two-photon imaging and has a low axial resolution of ~30 μm. F. Akhoundi et al. presented a compact MPM system which has an endoscopic imaging probe with a diameter of only 5 mm [88]. An Er-doped femtosecond fiber laser is utilized as the laser source, and a soliton self-frequency shift scheme is employed to convert the 1560-nm laser wavelength to 1700 nm to avoid the high water absorption around 1.5-μm wavelength. A double-clad fiber is utilized to deliver the excitation beam. The endoscopic imaging probe consists of the double cladding fiber, a piezoelectric scanning tube, and two aspheric lenses that work as a 15 miniaturized objective. SHG, THG, and 3PEF imaging are achieved with a large field of view (FOV) of >400 μm. However, due to the long excitation wavelength of 1700 nm, the system cannot excite intrinsic fluorophores and needs labeling for fluorescence imaging. Besides, the system has no depth scanning and has a slow image acquisition speed of 7–15 seconds per frame. In addition, the double-clad fiber also causes the pulsewidth to broaden from ~400 fs to ~800 fs, which can reduce the efficiency of nonlinear excitation. A. Li et al. presented an MPM with an endoscopic imaging probe that achieved a diameter of 6.5 mm [89]. A double-clad fiber is connected to the imaging probe to deliver both the excitation laser from the Ti:Sapphire source and the signal back to the detection unit. In a later publication, A. Li et al. demonstrated a handheld MPM imaging head with an endoscopic imaging needle which has a diameter of only 1.75 mm [90]. However, the system still relies on a bulky and complex Ti:Sapphire laser and the imaging modality is limited to 2PEF. The excitation laser is delivered to the handheld imaging head by a polarization-maintaining single-mode fiber, and the signal is delivered to the detection unit by a multimode fiber. There are several challenges to improve the compactness of an MPM system. The miniaturization of the laser source is one major factor in compact MPM design. Mode-locked Ti:Sapphire lasers are commonly utilized in traditional MPM systems as the source for the excitation beam [70]. The typical wavelength is around 800 nm with a tunable range from ~700 to ~1000 nm [91,92]. One major advantage of mode-locked Ti:Sapphire lasers is that they are tunable in wavelength for different imaging requirements. In addition, the pulsewidth can reach as short as ~10 fs. Such short pulsewidth increases the peak power of excitation pulses and hence brings about high efficiency in multiphoton excitation. However, mode-locked Ti:Sapphire lasers are bulky and expensive. It requires precise alignment and a constant temperature which is typically 16 maintained by water circulation. Therefore, Ti:Sapphire lasers, as well as other bulk lasers such as Cr:forsterite lasers, are not suitable for compact MPM microscopy/endoscopy systems required for clinical applications. With the advancement in fiber-based mode-lock lasers in the past few decades, femtosecond fiber lasers have become a choice for MPM imaging systems due to their advantages in compactness and cost [70,87,93–95]. The gain medium of a femtosecond fiber laser is an optical fiber doped with active elements such as erbium or ytterbium. The typical emitting wavelength of ytterbium- and erbium-doped fiber lasers are ~1050 nm and ~1580 nm, respectively. The long fundamental wavelength cannot excite most intrinsic fluorophores (such as NADH and FAD) for 2PEF, and sample staining is typically required for 2PEF imaging under such a wavelength. For erbium-doped fiber lasers, an alternative approach for label-free 2PEF imaging is using a frequency-doubling process that can generate a second harmonic beam with a halved wavelength for two-photon imaging. The pulsewidth of a mode-locked fiber laser can reach as short as sub-hundred fs (e.g. ~80 fs). While the wavelength of fiber lasers is not tunable, the compactness, solidness, and cost-efficiency of femtosecond fiber lasers still make them ideal for compact MPM imaging systems. Ytterbium-doped fiber lasers have been successfully demonstrated for label-free ex-vivo SHG and THG imaging of human skin [93,96]. Erbium-doped fiber lasers have also been utilized in label-free ex-vivo and in-vivo 2PEF, SHG, and THG imaging of animal and human skin and cancer tissues [82,97–99]. The objective is another important factor affecting compactness. Traditional microscope objectives typically contain a large number of lenses and a relatively bulky housing. While it provides excellent compensation for aberrations and a solid mechanical structure, the bulky design of the objective hinders the design of a handheld MPM imaging head or endoscopic probe. GRIN lens is an alternative to traditional objectives in compact MPM designs. However, the resolution 17 and field of view are often limited compared with microscope objectives. GRIN lenses may also exhibit higher optical aberrations, such as spherical aberration, compared to traditional lenses. In addition, depth scanning can be challenging for GRIN-based compact MPM systems. On the other hand, non-GRIN objectives can be specifically designed with fewer lenses and compact housing. The trade-off between imaging quality and compactness has to be considered and balanced during the design of miniaturized objectives. Scanning mechanisms also affect the compactness of the MPM system. In traditional MPM systems, galvo scanners are commonly used for planar (XY) scanning. However, galvo scanners are bulky and thus not suitable for miniaturized MPM systems. With the advancement in micro-electromechanical systems (MEMS), the advantage of compactness makes MEMS mirror an alternative approach for XY scanning in compact imaging systems. Single mirror MEMS has been demonstrated to achieve XY scanning in compact fiber-based MPM systems [70,97,100,101]. 1.7 Depth-resolved MPM As biological tissues have multiple layers and complex structures with disease-indicative information underneath the surface, depth-scanning is important for clinical applications of MPM imaging. In non-invasive skin imaging, depth-scanning is needed to acquire images from the dermis and hypodermis layers underneath the epidermis. In traditional microscope-based MPM systems, depth (Z) scanning is achieved by using motorized translation stages or piezo actuators to move either the sample or the objective. However, moving the sample is not feasible in clinical in-vivo imaging. When the sample position is fixed (where moving the sample using a translation stage is not feasible), piezoelectric actuators are commonly used to move the objective to achieve depth scanning in microscope-platform-based MPM systems [102–104]. A. Li et al. reported a 18 handheld imaging probe using a piezoelectric actuator for depth scanning [90]. While it can provide precise actuation of the objective, piezoelectric actuators are still generally large in size for a miniaturized handheld imaging head. In the system presented by A. Li [90], the actuator occupied a space of 22×17×10 mm3 before considering the size of the lens actuated by it. To achieve clinical MPM applications, alternative types of actuators are needed to minimize the system. In the handheld MPM imaging head demonstrated by A. Dilipkumar et al. [5], an electrically tunable lens (ETL) is mounted behind the objective to enable depth scanning. The ETL can change its shape to scan the focal plane. This design allows the fixed GRIN lens to work as an endoscopic probe while still enabling depth scanning. In Ref. [5], a depth scanning range of 200 μm has been achieved with a moderate axial resolution of 30 μm. However, ETLs can still be relatively large in size. For example, for a clear aperture of 3 and 11.6 mm, the diameter of the lens package can reach 11 and 30 mm, respectively (EL-3-10 and EL-12-30-TC, Optotune), let alone the controller. Also, according to the parameters of the GRIN lens (the ratio between the variation of imaging working distance and the variation of object working distance), the resulting tunable range of the focal plane (after the GRIN lens) may be much lower than the tunable range of the ETL. In addition, ETLs have a limited tuning range in short focal lengths [90,105]. Most ETLs are unable to achieve a focal length shorter than 70 mm [106], and that can add length to the imaging head. In recent years, shape-memory-alloy (SMA) actuators have been utilized in some imaging systems to support a compact design. Y. Wu et al. demonstrated an SMA depth scanner for MPM endoscopes [107]. However, this is an open-loop scanning system that has a low actuation accuracy of ~10 μm. As a later improvement, in a compact MPM demonstrated by A. Li et al. [89], 19 an endoscopic imaging probe with depth scanning enabled by a close-loop controlled SMA wire is demonstrated. However, the actuation speed of this SMA scanner is slow (~10 s actuation time in close-loop mode) and insufficient for clinical real-time imaging. While it can be challenging to develop a miniaturized depth scanner that also satisfies the needs for speed and accuracy, the SMA actuator does show great potential in this application. The actuation precision of SMA actuators is generally lower than piezoelectric actuators, nevertheless, it provides excellent compactness and can be integrated into a miniaturized objective. A more detailed literature review and discussion about the SMA actuated depth scanning is provided in Chapter 3. 1.8 Objectives and contributions The overall objective of this dissertation is to develop a compact MPM system suitable for clinical applications. While there have been lots of efforts in translating the MPM technique into clinical applications, there still remain a lot of challenges in this field as discussed above. I will be addressing those challenges mainly from three aspects: developing a compact MPM system, achieving depth-scanning, and acquiring two- and three-photon multimodal images simultaneously. A fiber laser source with dual wavelengths and a miniaturized SMA-actuated depth-scanning objective will be utilized to ensure compactness. The challenges include the design and fabrication of the miniaturized depth-scanning objective, the simultaneous utilization of two excitation wavelengths originating from a single fiber source, the simultaneous acquisition of two- and three-photon images, and the weak-signal multimodal image denoising and processing. This dissertation demonstrates theoretical analyses, system design and fabrication, image processing 20 methods, and multimodal imaging of various biological tissues. The challenges are discussed and addressed in this dissertation. The main contributions I made include: (1) Theoretical analyses of SHG and THG contrasts under a focused Gaussian excitation beam, including the impact of phase-matching and interface factors which can be applied to various focusing and sample conditions; (2) The opto-mechanical design and fabrication of the miniaturized depth-scanning objective, including part of the controlling software integration; (3) The development of an effective, fast, and low-profile denoising algorithm for low-signal MPM images; (4) The design and demonstration of simultaneous two- and three-photon MPM imaging, including the addressing of multimodal signal separation and crosstalk compensation. 1.9 Outline This dissertation is organized as follows: Chapter 2 presents theoretical analyses of harmonics generation (SHG and THG) with a focused Gaussian beam which provides a theoretical guide for multimodal MPM imaging in later chapters. The impact of phase matching on harmonic generation is discussed and analytical solutions are obtained for the integral of the phase-matching factor which can be applied to various focusing and sample conditions. SHG and THG are theoretically proven to be sensitive to interfaces, which provide interpretation to tissue-specific sources of contrasts in potential clinical multimodal MPM. An optimization process of the confocal parameter is proposed based on the properties of the sample. Simulations and experiments are demonstrated on layered structures to show the interface dependence for THG and SHG. The theoretical analysis provides a comprehensive understanding of harmonic generation from different samples. 21 In Chapter 3, a miniaturized depth-scanning objective is designed and fabricated based on SMA actuators. Optical and mechanical designs and depth-scanning calibrations of the miniaturized objective are demonstrated. The objective enables fast and accurate depth-scanning in compact MPM systems. Depth-resolved multimodal MPM is demonstrated with image stacks consisting of 2PEF, SHG, and THG signals acquired from various samples. In Chapter 4, a kernel-based nonlinear scaling denoising method is developed and introduced to denoise and improve the quality of MPM images. The denoising algorithm is especially effective on low-signal images and helps visualize weak-signal channels in merged multi-contrast images. Comparisons are demonstrated between our method and several commonly used denoising methods which include traditional methods and a self-supervised deep-learning method. In Chapter 5, a compact MPM system with simultaneous two- and three-photon imaging is demonstrated. With dual excitation wavelengths, multiple contrasts including 2PEF, SHG, and THG are acquired simultaneously from cytoplasm, collagen fibers, and interfaces, all label-free. Challenges related to the excitation by two wavelengths and the effective separation of two- and three-photon signals are discussed and addressed. With the depth-scanning objective and the denoising method both contributed, depth-resolved simultaneous two- and three-photon imaging is demonstrated on various tissue samples. Chapter 6 summarizes the studies and achievements and presents suggestions for potential future work. 22 Chapter 2: Theory of harmonic generation and impact of phase matching in multimodal multiphoton microscopy In this chapter, theoretical analyses are conducted to discuss the impact of phase matching and to show the interface sensitivity in SHG and THG imaging. Providing explanations and interpretations for tissue-specific sources of contrasts from SHG and THG, this theoretical study paves the way for multimodal MPM imaging in later chapters. This study also introduces an optimization method of the confocal parameter based on the properties of the sample, which can improve the imaging quality and potentially contribute to future clinical imaging. 2.1 Introduction Since the first introduction of 2PEF microscopy in 1990 [1], MPM has become a powerful technique for non-invasive and label-free imaging of biological tissues [2,4,108,109]. With the growing needs in research and clinical applications, MPM systems making use of multiple nonlinear effects have been developed and show great advantages in obtaining information-rich images with complementary tissue information acquired by different imaging modalities [101,110–112]. 2PEF, SHG, and THG are major signals utilized in multimodal MPM systems [109,113–115]. 2PEF requires fluorescence molecules and has been excited from NADH, FAD, elastin, etc. SHG is a second-order nonlinear effect and the second-order nonlinear susceptibility χ(2) requires materials with a noncentrosymmetric molecular structure, which is observed in fibrillar collagen. SHG has been widely used to image collagen in various tissue types such as skin, bone, tooth, cornea, etc. THG is a third-order nonlinear effect and the third-order nonlinear susceptibility χ(3) has no requirement on the molecular structure and all materials have nonvanishing third-order coefficients. Therefore, THG can complement 2PEF and SHG in imaging 23 other tissue structures that do not have fluorophores or noncentrosymmetric molecular structures. Nevertheless, THG microscopy has only been successfully acquired from interfaces, such as cell membrane and lipid/aqueous interfaces, due to the tight focusing of the Gaussian beam and Gouy phase shift (the phase change of a converging wave passing through a focus) [65,109,112,116]. As coherent signals, the efficiency of harmonic generation such as SHG and THG is determined by the pumping beam and the condition of phase-matching. In MPM, the nonlinear signals are typically excited by a tightly-focused femtosecond Gaussian laser beam. R. W. Boyd derived the mathematical expression of harmonic generation with a focused Gaussian beam [62], and reported that the efficiency of THG in the tight-focusing limit (e.g., in a bulk material) vanishes for the cases of normal dispersion (where Δk = qk1 − kq < 0, in which q is the harmonic order and k is the angular wavenumber) and perfect phase matching (Δk = 0) and is maximized through the use of a positive wavevector mismatch (Δk > 0). While tuning the phase-matching condition is possible in crystals with birefringence properties, it is difficult to do so in biological tissues which typically have normal dispersion (Δk < 0). Thus, the phase-matching condition for SHG and THG in biological tissues has been an important question. As fluorescence photons are summed up incoherently, 2PEF does not require phase-matching. Several papers have reported THG microscopy for imaging interfaces in tissue [117–119]. Y. Barad et al. showed efficient THG generation even in normally dispersive mediums where the nonlinear medium was not uniform such as near an interface, and THG was generated efficiently only when the interface was located within one confocal parameter from the focal plane [65]. J. Cheng et al. presented a theoretical study on THG from interfaces along the depth or lateral directions [120] by using Green’s function on three-dimensional sample structures. Thus, the existence of tissue interfaces can modify the requirement on the phase matching condition. 24 In SHG microscopy, several studies discussed phase-matching conditions for achieving backward-generated SHG. R. M. Williams et al. proposed a theory that SHG was generated from the shell of collagen fibrils rather than from its bulk [121]. J. Mertz proposed a theory of phase matching in backward-generated SHG by a periodic structure to provide negative momentum for compensating the large phase mismatch in the backward direction (Δkbackward = qk1 + kq) [122]. While those early works considered SHG detected in the epi-direction to be backward generated, an alternative theory is that SHG is generated in the forward direction and then scattered backward. Here we consider that SHG is generated in forward and then scattered backward, and its phase mismatch is Δk = qk1 − kq. J. A. C. Gomes et al. demonstrated experiments showing that SHG was generated from an interface but vanished in bulk silica [123]. Here we discuss SHG near an interface of a noncentrosymmetric material (χ(2) ≠ 0) under the tight-focusing condition. This is different from surface SHG where the symmetry is broken at the surface of a centrosymmetric material (normally χ(2) = 0) [62,124]. Furthermore, in order to optimize the pumping beam and the design of multimodal MPM systems and to better interpret MPM images, it is crucial to understand the origin of the nonlinear signals and the condition of phase-matching for efficient SHG and THG. The confocal parameter of the pumping beam is one major parameter that affects the harmonic generation and can potentially be optimized for tissue samples with given structures and phase-matching characteristics. B. Weigelin et al. brought up the idea of tuning the focal volume to optimize THG intensity in their review paper [109]. However, they did not discuss SHG and there was no quantitative method demonstrated for the optimization process. To obtain the optimal confocal parameter, a mathematical solution of harmonic generations that describes the relationship 25 between the harmonic power, phase mismatch, confocal parameter, and sample structure in a general condition is highly demanded. In this chapter, we will extend R. W. Boyd’s theory of harmonic generation under a focused Gaussian beam and derive analytical solutions of the phase-matching factor integral for both SHG and THG. The analytical solutions allow convenient and accurate analysis and simulation for harmonic generation in general conditions rather than only in special cases. Various cases with different phase mismatches, sample thicknesses, focal point positions, and confocal parameters are discussed. Our general solution is verified by comparing the results for special cases of tight-focus limit and plane-wave limit with known solutions in literature [62,65]. Based on the analytical solution of SHG and THG, the optimization of the confocal parameter of the pumping beam for given sample parameters is discussed. The interface dependence of SHG and THG is verified using simulations and experiments on layered structures. Spectral measurements are carried out to confirm SHG, THG, and 2PEF signals at their respective wavelength bands. Finally, multimodal MPM images consisting of SHG, THG, and 2PEF signals are acquired from biological samples to demonstrate the capability of multimodal MPM imaging. 2.2 Analytical derivation of THG and SHG using focused Gaussian beam Figure 2.1 shows a Gaussian laser beam at the fundamental wavelength propagating in the depth direction. The beam waist position is defined as zero location. The fundamental beam is focused into a nonlinear medium that extends from z0 to z. The confocal parameter b for a Gaussian beam is defined as the distance between the two points (symmetric to the beam waist) where the beam radius increased by a factor square root of 2 from the beam waist. 26 Figure 2.1 Illustration of the Gaussian pumping beam and the relative position of the nonlinear medium. We consider harmonic generation under the undepleted-pumping assumption and paraxial approximation. The power carried by the qth harmonic Gaussian beam can be expressed as [62] , (2.1) where nq is the refractive index of the medium for qth harmonic wavelength, ε0 is the vacuum permittivity, c is the speed of light in vacuum, and w0q is the beam waist radius of the qth harmonic. Aq(z) is a function of z given by [62,65] , (2.2) where ω is the angular frequency of the fundamental wave, and A1 is a factor related to the complex amplitude of the fundamental wave. J′ is an integration given by [62,65] , (2.3) where z0 and z represent the entrance and end of the nonlinear medium. Δk is the phase mismatch given by Δk = qk1 − kq and ki is the angular wavenumber given by ki = 2π/λi, where λi is the wavelength and i = 1 or q. The subscript 1 or q indicates the fundamental or qth harmonic wave, respectively. χ(q) is the qth-order nonlinear susceptibility of the medium. b is the confocal parameter given by b = kw02, where w0 is the beam waist radius. Harmonics share the same confocal parameter as the fundamental wave [15]. J′ is proportional to the amplitude of the generated harmonic and can be used to represent the efficiency of harmonic generation. 220 0 ( )q q q qP n c w A ze p=1 0( ) ' ( , , )2qq qqiqA z A J k z zn cw= D0( ) '0 1( ') '' ( , , )2 '1q i kzzq qzz e dzJ k z zizbc D-D =æ ö+ç ÷è øò 27 For uniform medium, χ(q)(z′) reduces to a constant and can be moved out of the integration as , (2.4) where the phase-matching factor Jq is given by , (2.5) In situations where the excitation beam is focused into a sample consisting of multiple layers of materials, χ(q)(z′) can be expressed by a step function. Within each layer, χ(q) can be treated as a constant, and Eq. (2.4) and Eq. (2.5) can be used to simplify the integration shown in Eq. (2.3). For THG (q = 3), the integration Eq. (2.5) can be solved analytically as . (2.6) Here Ei(x) is the Exponential Integral function defined by [125] , (2.7) where x is a complex variable that depends on b, Δk, and z′. Eqs. (2.6) and (2.7) are suitable for Δk > 0. Under the circumstance of normal dispersion where Δk < 0, the following transfer is adopted to avoid discontinuity in the Exponential Integral function as , (2.8) where E1(x) is a variation of the Exponential Integral given by [125] ( )0 0' ( , , ) ( , , )qq qJ k z z J k z zcD = D0'0 1'( , , )2 '1i kzzq qze dzJ k z zizbD-D =æ ö+ç ÷è øò0'' 223 0'( , , ) Ei '2 ' 4 22 1z zb ki kzz zibe ib k b kJ k z z i kz eizb=DD -=D Dæ öD = - + Dç ÷æ ö è ø+ç ÷è øEi( )txex dtt-¥-= -ò1( ) Ei( )E x x- - = 28 . (2.9) Ei(x) and E1(x) can be computed by [125] (2.10) and , (2.11) where γ is the Euler-Mascheroni constant. Ei(x) and E1(x) are standard built-in functions in various mathematical software such as Matlab, Mathematica, and MathStudio, and can be computed with complex variable input. Similarly, for SHG (q = 2), the integration Eq. (2.5) can be solved as , (2.12) for positive Δk. The transfer given by Eq. (2.8) should be utilized for negative Δk. The analytical solutions of J for THG and SHG allow convenient and accurate analyses for THG and SHG efficiencies in general conditions and provide a useful approach to improve MPM designs in future studies. 2.3 Analyzing THG and SHG efficiencies 2.3.1 THG and SHG in various sample thickness cases Various situations with different settings in the pumping beam and sample structures can be discussed using the results of Eq. (2.6) and (2.12) for THG and SHG, respectively. Figure 2.2 1( )txeE x dtt-¥= ò11 2211 0( 1) 1Ei( ) ln( )!2 2 1sx s sss mxx x es mg--¥-= =-= + ++å å11( )( ) ln( )( !)ssxE x xs sg¥=-= - - -å0'22 0'( , , ) Ei '2 2z zb kz zib b kJ k z z i kz e=D-=Dæ öD = - + Dç ÷è ø 29 shows |J| as a function of the phase-mismatch parameter Δk varying from negative to positive for bulk tissue, tissue interface, and tissue of finite thickness, respectively. In the case that the fundamental beam is tightly focused within a bulk nonlinear medium as b ≪ |z0| and b ≪ |z|, the integral interval [z0, z] in Eq. (2.5) evolves to [−∞, +∞]. With this interval of [−∞, +∞], Eq. (2.6) and (2.12) can be plotted in Figure 2.2(a). It can be seen that the efficiency of THG and SHG vanishes on the negative phase mismatch side (normal dispersion). For perfectly phase-matched (Δk = 0) materials, the efficiency of SHG reaches its peak while the efficiency of THG still vanishes. For abnormal dispersive materials, the efficiency of SHG decreases with bΔk while the efficiency of THG peaks at bΔk = 2. Under the tight-focus limit, the result of our general solution is consistent with that reported in literature [62,65]. In practice, the bulk material case applies to a relatively uniform sample with a thickness that is much larger than the confocal parameter of the excitation beam. As a result, no THG or SHG can be obtained from a bulk material with normal dispersion (which is the case for most materials) under tight focusing. In Figure 2.2(b), the pumping beam is focused on the interface of a bulk nonlinear medium. For z′ < 0, there are χ(2) = 0 and χ(3) = 0, while for z′ > 0, there are non-zero χ(2) and χ(3). As shown in Figure 2.2(b), there are non-vanishing efficiencies for both THG and SHG even in the normal dispersion region where Δk < 0. Maximum THG efficiency is obtained under abnormal dispersion where bΔk ≈ 0.875 and maximum SHG efficiency is obtained when a perfect phase match is achieved. This shows that THG and SHG can be obtained from the interface of a bulk material even under tight focusing. 30 Figure 2.2 Dependence of factor |J| on the normalized phase-mismatch parameter bΔk, under conditions (a) bulk material (tight focusing limit) (z0 = −∞, z = +∞), (b) focusing on an interface (z0 = 0, z = +∞), (c) focusing inside a thin slab (z0 = −L/2, z = +L/2), and (d) focusing on the surface of a thin slab (z0 = 0, z = +L). Figure 2.2(c) shows harmonic generation from a thin slab (comparable to or thinner than the confocal parameter) of a nonlinear medium where there are non-zero χ(2) and χ(3) within its thickness of L. The focal plane of the pumping beam is at the center of the medium and the integral interval [z0, z] in Eq. (2.5) is [−L/2, +L/2]. Figure 2.2(c) shows the relationship between |J/b| and bΔk for sample thicknesses of L = b, L = b/2, and L = b/5, respectively. When the sample is a thin slab, the efficiencies of THG and SHG do not vanish even for Δk < 0. As the sample thickness gets thinner, the THG and SHG efficiency curves get broader, indicating a more relaxed phase-matching requirement. This trend continues and leads to the plane-wave limit when L ≪ b. This can be understood that the wavefront of a Gaussian beam near the beam waist approaches to a plane wave. The consistency between our general solution under the limit of L ≪ b and literature 31 results [62,65] under the plane-wave limit is a validation of our analytical solution. Even though the maximum efficiency occurs under slightly positive Δk, reasonable THG and SHG efficiencies can be generated in normally dispersive materials without the need for perfect phase-matching. Figure 2.2(d) shows a similar case as in Figure 2.2(c) except that the excitation beam is focused on the interface of the thin slab and the integration range is set from z0 = 0 to z = +L. The results in Figure 2.2(c) and Figure 2.2(d) are quite similar, likely because the sample is thin. When focusing on the interface, the efficiencies of THG and SHG are also shown to not vanish for Δk < 0, similar to the case when focusing inside the thin slab. Some minor differences are observed when focusing on the interface. For example, the peak J factor is slightly reduced; the J factor does not go down to zero at certain values of bΔk; and the positive and negative bΔk sides show more asymmetry. The difference between focusing on the interface versus inside a thin slab reduces as the slab gets thinner. Our analytical and simulation results can provide some insights into THG and SHG microscopy. For very thin samples (e.g., L ≤ b) such as collagen fibrils or cell membranes, no phase matching is required for generating reasonable SHG and THG. This is consistent with that SHG and THG have been reported over a broad range of excitation wavelengths in biological tissues without the intention of doing phase matching. When the sample is uniform and relatively thick compared to the confocal parameter (e.g., L > 10b), the THG and SHG efficiencies will tend to vanish (reduce to considerably low) for the normally dispersive material when the excitation beam is focused inside the sample. However, focusing on the interface will still allow the generation of reasonable THG and SHG. 32 2.3.2 Optimization of the confocal parameter b The relationship between bΔk, L, and |J|, in turn, can be utilized to optimize the confocal parameter b for a given sample with known or predictable thickness and phase mismatch. The expression of the qth-harmonic power can be derived by substituting Aq(z) in Eq. (2.1) with Eq. (2.2), and we get , (2.13) where |A1| can be substituted by the Gaussian beam power equation as , (2.14) and therefore, the power of qth harmonic can be derived as , (2.15) using the relationship of b = kw02, where P1 is the power of the fundamental beam. For a pumping beam with a given wavelength and power, the confocal parameter b can be optimized for qth-harmonic power by finding the maximum of Pq′ given by . (2.16) Figure 2.3 shows examples of the optimization process of b for several given sample settings with positive and negative phase mismatches and various sample thicknesses. In these examples, the focal plane is placed at the center of the nonlinear medium in depth. The optimal ratio of b/L can be found where the curve of P′ reaches its peak. As we can see, the optimum b/L ( )220 0 1 0' , ,2qq q q qqiqP n c w A J k z zn cwe p= D1211 21 0 01PAn c we pæ ö= ç ÷è ø22 111 11 1 11 0 2'4qqq qq q q qqq k JP Pn n c bwe p--- + -=212''q qJPb-= 33 ratio depends on the phase mismatch Δk and the sample thickness L, and the peak is typically located between ~0.3–1. Thus, for efficient SHG and THG excitation, the confocal parameter should be selected to be equal to or slightly smaller than the thickness of the nonlinear medium being imaged. Previously, G. D. Boyd et al. found that the highest efficiency for SHG is achieved when the incident beam is focused at the center of the nonlinear medium, L/b is set to be 2.84, and Δk is set to be 3.2/L [62,126]. Here as shown in Figure 2.3, when L = 5 μm and Δk = 3.2/L = 0.64 μm−1, the optimal confocal parameter is found at b/L ≈ 0.357 (L/b ≈ 2.8), which is consistent with the previously reported result in literature. Nevertheless, our analytical solution can be applied to find the optimum confocal parameter for various sample conditions. Figure 2.3 Dependence of harmonic power factor P′ on normalized confocal parameter b/L under various sample conditions. A similar relationship can be presented between harmonic generation and NA. For a Gaussian beam, the far-field divergence half-angle θD is given by [127,128] 34 . (2.17) Under paraxial approximation in which NA ≈ sin(θD) ≈ θD, Eq. (2.17) becomes , (2.18) where NA is the numeric aperture. Bringing Eq. (2.18) into b = kw02, we get . (2.19) Bringing Eq. (2.19) into the above calculations, the relationship between harmonic generation and NA can be derived. As the optimal b/L is typically ~0.3–1, here the optimal NA2L is ~1–3.4 μm for 1580 nm and ~0.5–1.7 μm for 790 nm. As an example, for a sample with L = 5 μm and Δk = -0.5 μm−1, the optimal b is ~1.39 for SHG and ~1.88 for THG, and the optimal NA can be derived as 0.60 for SHG excited by 790 nm and 0.73 for THG excited by 1580 nm. 2.4 Simulation and experimental validation using layered structures To investigate the impact of tight-focusing on bulk material and interfaces, samples constructed with layer materials are studied. Both simulations and experiments of SHG and THG are carried out. In experiments, the SHG and THG signals are collected in the backward direction. Figure 2.4 shows the layered structure and results for THG. A water layer (χ(3)water = 1.83 × 10−22 m2V−2 [129]) with a thickness of 150 μm has been sandwiched between two glass slides (here we use the parameters of BK7 to represent the glass which has χ(3)BK7 = 2.98 × 10−22 m2V−2 [129]). For water, there are n1 = 1.317, n3 = 1.334 [130], and that results in Δk = −0.203 μm−1; and for BK7 glass, there are n1 = 1.500, n3 = 1.520 [131], and that results in Δk = −0.239 μm−1. The THG experiment is carried out on a fiber-laser-based compact MPM system [66]. The 0D wlqp=0w NAlp=22bNAlp= 35 excitation wavelength is 1580 nm and the objective is a custom water-dipping lens with an NA of 0.53. The confocal parameter b is estimated to be ~10 μm based on the depth of focus for the 0.53-NA lens. THG signal is recorded as the focal plane of the excitation beam scans through the sample in the depth direction. Figure 2.4 Variation of THG intensity as the focal plane scans through a glass-water-glass layered structure. (a) Simulated result of P3/P13. (b) Experimentally measured THG intensity. As both the linear and nonlinear properties of water and glass differ, the glass-water-glass interfaces enable the excitation of THG. Figure 2.4(a) shows the simulated result of P3/P13 versus the focal plane position. It shows that the THG efficiency peaks when the focal plane is at the interfaces. The efficiency of THG vanishes when the focal plane falls inside the bulk part of the 36 water layer. Figure 2.4(b) shows the experimental results for THG from this layered structure. The experiment matches well with the simulation. The two measured THG intensity peaks appear where the focal plane is scanned through the two interfaces, respectively. THG is only observed near the interfaces and no THG signal is observed when light is focused inside the bulk part of the water layer. In the THG experiment, the second peak at a deeper depth shows significantly lower intensity than the first peak. Multiple factors can contribute to this intensity reduction. 1) Water has a substantial absorption coefficient of ~20 cm−1 around 1500-nm wavelength [132,133]. For the 150-μm thick water layer, absorption can cause the power of the fundamental beam to reduce by ~26%. This will result in a ~60% reduction in the THG signal due to the cubic relationship between the third harmonic and the excitation power. 2) The backward-detected THG is generated in the forward direction and then back-scattered. Thus, the difference in the backward scattering conditions also affects the THG intensity. 3) Even though the custom water-dipping lens provides low spherical aberration when focusing into tissue (tissue has refractive indexes close to water), the glass layer in the phantom can contribute to index mismatch. Spherical aberration from a mismatch in the refractive index could deteriorate the resolution and signal intensity at deeper depths [134,135]. Results of SHG from a layered structure are shown in Figure 2.5. A photonic chip is utilized as the sample which has a layered structure of SiO2-Si-SiO2, with thicknesses of 2.5, 0.22, and 2.0 μm, respectively. SiO2 has a non-vanishing χ(2) of 0.92 × 10−12 mV−1 [136], while at the same time, it has no birefringence and thus cannot achieve phase matching. Si is centrally symmetric and thus has zero χ(2). For SiO2, there are n1 = 1.454, n2 = 1.471 [137], and that results in Δk = −0.270 μm−1. Due to the thin thickness of this sample, the experiment is carried out on a bench-top MPM system 37 based on a Ti:Sapphire laser with a central wavelength of 800 nm. To demonstrate the impact of confocal parameters, SHG is acquired by two water-immersion objectives from Olympus UPlanSApo (60×, NA = 1.2) and LUMPlanFLN (40×, NA = 0.8). The confocal parameters are estimated to be ~1 μm and ~3 μm, respectively, based on the depth of focus for the 1.2- and 0.8-NA objectives. Figure 2.5(a) shows the simulated results of P2/P12 versus the focal plane position under b = 1 μm and b = 3 μm. Two SHG peaks are observed when the focal plane is at the two interfaces of entering the first and exiting the second SiO2 layer, respectively. SHG signal dips in the middle where the focal plane is away from those two interfaces. The 0.22-μm Si gap enhances the dip between the two peaks but is not able to result in the differentiation of additional interfaces. While b = 1 μm shows higher resolution, b = 3 μm provides a higher power factor due to its longer interaction length. Figure 2.5(b) shows the experimental results for SHG. With the NA = 1.2 objective, the resolution is higher and two SHG peaks are clearly observed at the two interfaces that match well with the simulation. With the NA = 0.8 objective, the two peaks are not resolved due to the lower resolution. To reliably compare the SHG intensity caused by different b parameters, a calibration process is carried out by imaging fluorescent beads and then normalizing SHG to the corresponding 2PEF intensity for each objective. The calibration cancels out other factors resulting from switching the objectives, such as power on the sample, etc. Figure 2.5(c) shows simulated harmonic power factor P′ versus confocal parameter b based on our analytical solution. The maximum SHG power factor is predicted to be at b = 4.64 μm (corresponds to b/L = 0.983). As b = 3 μm is closer to the calculated optimum confocal parameter, the harmonic power should be higher than that with b = 1 μm. This is confirmed by the experiment in Figure 2.5(b). 38 Figure 2.5 SHG from a SiO2-Si-SiO2 layered structure under different confocal parameters. (a) Simulated result of P2/P12. (b) Experimentally measured SHG intensity. (c) Calculated harmonic power factor P′ versus confocal parameter b under L = 4.72 μm. The simulation and experiment on layered structures confirm our results that SHG and THG require interfaces or thin samples under the tight focusing condition. Optimizing confocal parameters for harmonic efficiency is demonstrated using SHG imaging by two objectives with 39 different NAs. Although choosing a confocal parameter close to the sample length can increase the harmonic efficiency, a smaller b parameter can provide a higher resolution. Thus, a balance between harmonic efficiency and image resolution will be needed. 2.5 Demonstration of multimodal MPM imaging SHG, THG, and 2PEF can be utilized to acquire complementary information about tissues. To demonstrate the capability of multimodal MPM imaging, images are acquired from various biological samples. The imaging is conducted on a depth-scanning fiber-laser-based multimodal MPM system [66]. It contains dual excitation wavelengths by frequency doubling an Er-doped femtosecond fiber laser. The SHG and 2PEF are excited by ~790 nm and THG is excited by ~1580 nm. The excitation beams are focused into the sample by a custom water-dipping objective with an NA of ~0.53 [66,138,139]. A commercial objective is not used because objectives off the shelf do not have a working wavelength ranging from 400 to 1580 nm. With the working wavelength prioritized for signals (400–650 nm), the multi-lens design of commercial objectives results in significantly low throughput for 1580 nm, which further reduces the (already weak) THG signals. The MPM system has a two-photon lateral and depth resolution of 0.9 μm and 10 μm, respectively, and a three-photon lateral and depth resolution of 1.4 μm and 15 μm, respectively [66]. There is a focal shift of ~80 μm between the two- and three-photon images due to their different excitation wavelengths, which is compensated in post-processing for merging the channels. The SHG, THG, and 2PEF signals are confirmed by measuring spectra using a spectrometer consisting of a spectrograph (SP-2150i, Princeton Instruments) and an electron-multiplying charge-coupled-device (EMCCD) camera (ProEM 512, Princeton Instruments). Figure 2.6 shows the spectra and multimodal MPM images acquired from a photonic chip. Figure 40 2.6(b) shows the measured spectra of two- and three-photon signals, respectively. The SHG and THG peaks are observed at ~402 nm and ~516 nm, respectively. The SHG and THG peaks are not exactly 1/2 of 790 nm and 1/3 of 1580 nm, respectively, because as shown in Figure 2.6(a), the laser outputs are relatively broadband and spectra have a lot of structures, and the peaks are not exact 790 and 1580 nm. This is common for femtosecond fiber lasers. 2PEF shows a broad band ranging from 400 to 650 nm. Figure 2.6(c) shows the merged multimodal image of the photonic chip. Strong THG is observed in the taper region due to the interfaces of SiO2-Si-SiO2 layers. SHG is strong in the grating coupler region where the grating pattern may increase backscattering for the backward detected SHG. The THG and SHG signals from the photonic chip depend on the nonlinear coefficients of Si and SiO2, confocal parameters, and conditions for backscattering. Figure 2.6 Two- and three-photon MPM imaging of photonic chip. (a) Measured laser fundamental and frequency-doubled spectra for THG and SHG excitation, respectively. (b) Spectra of SHG and 2PEF excited by 790 nm and THG excited by 1580 nm. (c) SHG/THG/2PEF image of photonic chip. Three regions: (1) grating coupler; (2) adiabatic taper; (3) photonic nanowire. The magenta area is an overlap of SHG and 2PEF. Scale bar is 20 μm. 41 Figure 2.7 Multimodal MPM images of (a) human tooth and (b) mouse femur bone at different depths. Red – 2PEF; blue – SHG; green – THG. Scale bar is 40 μm. 42 Figure 2.7 demonstrates label-free multimodal MPM imaging on ex-vivo biological samples. The laser power applied on samples is ~25 mW and ~50 mW for the 790-nm and 1580-nm wavelengths, respectively. All 2PEF signals come from autofluorescence. Figure 2.7(a) shows an image stack of a human tooth acquired from different depths up to ~300 μm deep. The image stack is taken from the dental cervix (tooth neck) where the enamel layer is thin. Enamel is responsible for the 2PEF signal and can be observed within ~90 μm from the surface. THG signals can be observed from the surface to ~140 μm deep. SHG signal reveals the tubular nature of dentin (with a diameter of around ~3 μm [140]) and can be observed starting from ~150 μm beneath the surface. Figure 2.7 (b) shows an image stack of a mouse femur bone acquired from different depths up to ~140 μm deep. The thick strip-shaped structure providing 2PEF signals at the depth of ~50 μm is likely a cartilage canal [141]. The lacuna-canalicular framework can be observed from the images. Oval-shaped structures showing THG signals at depths ranging from 60−110 μm indicate osteocytes [142,143]. The THG signal could originate from the interfaces created by the gap of 0.5–1.0 μm between the osteocyte cell body and the lacunar wall [144,145]. As we can see, multimodal imaging by SHG, THG, and 2PEF shows complementary information about tissues. The current images are acquired by an objective of NA = 0.53 as limited by the availability of high NA miniature objectives. Higher NA objectives may be helpful to improve the resolution and harmonic efficiency in the future for imaging tissues that normally have small features on the nanometer to micrometer scales. 43 2.6 Discussion and conclusion MPM using SHG, THG, and 2PEF contrasts has been widely used in biological tissues. While THG contrast is known to mainly come from tissue interfaces such as the lipid/aqueous interfaces due to the impact of tight-focusing and Gouy phase shift, such impact on SHG imaging has not been recognized. Our study shows that this impact applies similarly to SHG. For tightly-focused Gaussian beams in bulk tissue, THG and SHG vanish in the normal dispersion region where Δk < 0 (which is the case for most materials). Nevertheless, at tissue interfaces or for very thin samples, the phase-matching condition is greatly relaxed and SHG and THG can be generated even in normally dispersive materials. The relaxed phase-matching condition is consistent with the excitation of SHG and THG in tissues with a wide range of wavelengths. SHG contrast is found in fibrillar collagen (e.g., type I) but not in non-fibrous collagen (e.g., type IV) [146–148]. It is possible that the fibrous structure provides the interfaces. In summary, harmonic generation using a focused Gaussian beam has been studied with the phase-matching factor Jq solved analytically. The results show that while the efficiency of harmonic generation vanishes for infinite uniform mediums with normal dispersion, harmonics can be generated when there is an interface that breaks the uniformity. An optimization process of the confocal parameter is demonstrated based on the properties of the sample. Simulated and experimental results demonstrate the interface dependence for THG and SHG from layered structures. Label-free multimodal MPM imaging with SHG, THG, and 2PEF is demonstrated on biological samples. Complementary information of various tissue structures is revealed by the multiple contrasts. The theoretical analyses and simulations presented in this chapter provide a comprehensive understanding of harmonic generation from thick and thin samples and provide an approach for improving the optical designs of MPM systems in future studies. 44 Chapter 3: Depth scanning multimodal MPM realized by SMA-actuated miniaturized objective and dual excitation wavelengths In this chapter, in order to miniaturize the objective and depth-scanning mechanism for a potential clinical handheld imaging head, an SMA-actuated depth-scanning objective is designed and fabricated. The miniaturized depth-scanning objective is demonstrated on a compact multimodal MPM system with a dual-wavelength Er fiber source and a miniaturized MEMS scanner. 2PEF, SHG, and THG image stacks are acquired from animal and plant tissues at various depths. 3.1 Introduction As discussed in Chapter 1, compact MPM systems capable of acquiring depth-resolved image stacks in multiple contrasts are highly desirable for label-free in-vivo imaging. As the excitation beam is focused into the sample and nonlinear signals are generated only from the highly localized focal volume with high laser power density, MPM provides high resolution in both the XY plane and the axial (depth, Z) direction [52,138]. The depth-resolved imaging allows the differentiation of tissue layers and reveals detailed structures underneath the tissue surface [52,149]. While XY scanning in MPM endoscopy has been reported using piezoelectric tubes and microelectromechanical (MEMS) scanners [90,150–152], achieving depth scanning has been a challenge due to the requirement of compact size and precision actuation in the axial direction. Motorized translation stages have been demonstrated in previous research work but are considered not suitable for clinical applications due to their bulky size [151]. Piezoelectric actuators have been reported to be utilized to realize depth scanning [105,153]. In 2019, A. Dilipkumar et al. demonstrated a two-photon imaging system with an ETL based depth scanning and a GRIN lens objective [5]. However, the NA of the GRIN lens objective was relatively low 45 (NA = 0.38) and the axial resolution was over 30 μm with a scanning range limited to 200 μm [5]. Besides, the ETL showed a significantly deteriorated linearity and precision in focal positions under high current (short focal length). Moreover, ETLs have a limited focusing power tuning range of 10 to 25 diopters typically [90], which limits its use in short-focusing applications and prevents it from being integrated into the objective [153]. In 2019, A. Li et al. reported a two-photon imaging probe with depth scanning realized by a piezoelectric stage [90]. A GRIN lens with a relatively high NA of 0.8 was adopted but the FOV in the XY plane was limited to ~120 μm, and the penetration was also limited to 200 μm [90]. The piezoelectric stage (PP-18, Micronix) has a size of 22 × 17 × 10 mm3, and when the lens is mounted it makes the module size even larger. Due to the small electrically induced strains, piezoelectric actuators require displacement magnification configurations such as cantilever-type benders, which occupy space and render the actuator bulky in size [105]. SMA actuators can provide potential improvement to these limitations when utilized for depth scanning. In an SMA actuator, current is applied to an SMA wire where Joule heating causes the phase transformation of the malleable crystalline structure, and the movement of the actuator is determined by the equilibrium of a counterforce (e.g., provided by a spring) and the SMA wire [154,155]. Compared with piezo-based or ETL-based scanning, SMA actuators provide reduced device size and improved scanning range at a lower cost [156,157]. SMA actuators also have the advantage of operating at very low voltage (<5 V) with a relatively fast response speed (order of 10–100 ms). In 2010, Y. Wu et al. presented an SMA-based depth-scanner with an actuation range of 150 μm [107]. However, the scanner was open-loop operated and the accuracy was limited to ~10 μm, and the one-way full-range scanning took ~1 s [107]. In 2017, A. Li et al. demonstrated a closed-loop controlled SMA depth scanner for MPM [89]. In this design, they 46 improved the travel range to 490 μm. However, the scanning speed declined significantly under closed-loop control and required ~10 s to reach the target position [89]. It can be seen that in previous research, the trade-off of speed and accuracy limited the development of SMA-actuated depth-scanning MPM systems and a faster miniaturized closed-loop SMA actuator can increase the potential of SMA actuators in this application. On the other hand, multimodal imaging is critical for achieving label-free MPM imaging because different nonlinear effects such as 2PEF, SHG, 3PEF, and THG reveal different biochemical properties and tissue structures [101,110–112,118,158]. 2PEF signals are generated from intrinsic fluorophores such as NADH, FAD, and elastin [61]. SHG signals are generated from ordered non-centrosymmetric structures such as collagen fibers [159]. 3PEF also excites fluorescence signals but with lower efficiency than 2PEF. THG signals are related to heterogeneous boundaries such as aqueous-lipidic interfaces and air-aqueous interfaces [65,101]. Especially, adding THG to multimodal MPM can provide important information on tissue structures that lack 2PEF or SHG contrast, such as lipids and interfaces [99,160]. Therefore, by combining both two-photon (e.g. 2PEF and SHG) and three-photon (e.g. THG) imaging, complementary information can be obtained from multiple channels and be merged to form information-richer images. Coherent anti-Stokes Raman scattering (CARS) is another type of nonlinear microscopy that can detect the vibrational signatures of molecules [161,162]. CARS has been utilized for non-invasive imaging of lipids in biological samples [4,161]. Compared with THG, CARS requires two laser sources for the pump and probe light, respectively. Exciting both two-photon and three-photon signals is a challenge due to the involvement of various wavelengths. To prevent the potential THG signal from entering the ultraviolet range, excitation lasers with a long wavelength (>1200 nm) are typically required [70]. In 2018, F. 47 Akhoundi et al. demonstrated a multimodal MPM probe (for SHG, THG, and 3PEF) with an excitation wavelength of 1700 nm [88]. While SHG and THG images were acquired successfully from unstained samples and 3PEF images were acquired successfully from stained samples, the 1700-nm laser was not able to excite intrinsic 2PEF signals, and furthermore, there was no automatic depth-scanning capability in their system. To excite common endogenous fluorophores in tissue for label-free 2PEF imaging, a shorter excitation wavelength (<900 nm) is typically required [70,113]. Therefore, the scheme of dual excitation wavelengths has become an important approach to acquiring both intrinsic 2PEF signals and three-photon signals. In 2018, A. Filippi et al. demonstrated multimodal label-free ex-vivo MPM imaging using dual excitation wavelengths of 800 nm (for 2PEF and SHG imaging) and 1200 nm (for THG imaging) but without depth scanning [163]. In 2020, our team reported a multimodal MPM system with dual excitation wavelengths for intrinsic 2PEF, SHG, and THG imaging but still without depth scanning capability [101]. In addition, the single-lens objective working in air limits its penetration depth [66,139]. On the packaging of the system, it is still relatively bulky for clinical applications. In this chapter, we demonstrate a compact MPM system with both depth scanning and multimodal imaging capabilities. The depth scanning feature is enabled by a miniaturized SMA-based objective. The SMA actuator provides a relatively long scan range, high response speed, and actuate positioning at a miniature device size. Our custom-designed depth-scanning objective has a balance between the FOV and NA. The multimodal imaging capability is realized by the dual excitation wavelengths of 790 and 1580 nm from a single erbium-doped fiber laser. The dual-wavelength excitation scheme ensures that intrinsic 2PEF, SHG, and THG signals can be all acquired label-free from the sample. The compact all-fiber femtosecond excitation source further improves the mobility of this MPM system for potential clinical use. Image stacks of 2PEF, SHG, 48 and THG signals at various depths are acquired from several tissue samples to demonstrate the capability of depth-resolved multimodal label-free imaging. 3.2 Experimental setup Figure 3.1 shows the setup of the multimodal multiphoton microscopic imaging system. A home-built 1580 nm mode-locked Er-doped fiber laser with an average output power of ~230 mW, a pulsewidth of ~80 fs, and a repetition rate of 47 MHz is employed as the excitation light source [164]. A periodically poled MgO:LiNbO3 (PPLN) crystal (MSHG1550-0.5-0.3, Covesion) converts the 1580 nm pulse to 790 nm with an efficiency of ~35%. The PPLN has a broad acceptance bandwidth of 42 nm which maintains the pulsewidth at ~80 fs for the 790 nm wavelength [164]. The 790 nm wavelength is utilized to excite 2PEF and SHG signals while the 1580 nm wavelength is utilized to excite the THG signal. Convex lenses L1 and L2 with the focal length of fL1 = fL2 = 7.5 mm serve to focus the laser beam into the PPLN and to collimate the beam emitted out from the PPLN, respectively. Two achromatic lenses, L3 and L4, serve as the scan lens and the tube lens, respectively, of the microscope. The scan lens has a focal length of fL3 = 25 mm and the tube lens has a focal length of fL4 = 50 mm. A gold-plated bonded MEMS mirror (13Z2.1-2400, Mirrorcle Technologies) with a diameter of Φ = 2.4 mm is employed for XY scanning. The overall size for the MEMS module is ~15×20×5 mm3. The MEMS mirror has a resonance frequency of 1.6 kHz. For scanning a 2D frame of 512×512 pixels, a maximum speed of ~4 FPS at a pixel dwell time of 1 μs can be achieved. However, for increasing the SNR, the pixel dwell time in the experiments is set to be 10 μs and that results in a scanning speed of ~0.4 FPS. 49 Figure 3.1 Setup of the multimodal MPM imaging system. L – lens; PPLN – periodically poled MgO:LiNbO3; F – filter; MEMS – microelectromechanical system; OBJ – objective lens pack; MMF – multimode fiber; DBS – dichroic beam splitter; PMT – photomultiplier tube; HV – high voltage; DAQ – data acquisition; PC – personal computer; SMA – shape memory alloy; PCIe – peripheral component interconnect express; I2C – inter-integrated circuit. A custom-designed SMA-actuated depth-scanning objective consisting of an aspheric lens (352080, LightPath) and a plano-convex lens (45469, Edmund Optics) is designed to minimize spherical aberration [139]. Figure 3.2 shows the objective lens layout and Zemax simulation of root-mean-square (RMS) wavefront error versus field of view. The aspheric lens has an effective focal length of fAL = 3.9 mm and an NA of 0.55. The plano-convex lens has an effective focal length of fCL = 18 mm and an NA of 0.17. The effective focal length of the compound objective is fobj = 3.6 mm and the NA is calculated by Zemax to be 0.53. At both 790 and 1580 nm, the RMS wavefront error remains below diffraction-limited within the ~0.15 mm field of view (one side). During depth imaging, the aspheric lens is actuated by the SMA actuator, and the sample is immersed in water. The depth-scanning SMA actuator is controlled by an Arduino microcontroller (Mega 2560, Arduino) through the I2C communication protocol. 50 Figure 3.2 Design of the objective. (a) Optical layout; (b) Zemax simulation of RMS wavefront error vs. field of view. BM – laser beam; AL – aspheric lens; AR – air; CL – plano-convex lens; WT – water (water is used as a sample in simulation); DL – diffraction-limited RMS wavefront error. Figure 3.3 Relative irradiance (Strehl ratio) and relative harmonic intensity vs. field of view for the objective. (a) 790-nm excitation wavelength and SHG; (b) 1580-nm excitation wavelength and THG. 51 Figure 3.3 shows how SHG and THG signal intensities change as a function of the field of view. The point spread function (PSF) is calculated by Zemax over the field of view (from the center to 0.15 mm away). The relative irradiance (Strehl ratio) is extracted from the peak value of the PSF at different focal point locations. The relationship between the relative irradiance and FOV is then plotted for the 790- and 1580-nm excitation wavelengths. As SHG and THG intensities are proportional to the square and cube of the incident intensity, respectively, the relative intensities of SHG and THG are calculated as the square and cube of the 790- and 1580-nm relative irradiance values, respectively, over the FOV. The Strehl ratio is the highest at the center of the FOV and on the edge of FOV, the Strehl ratio drops due to higher aberrations. Comparing 790 nm and 1580 nm, the Strehl ratio for the 790 nm decreases more significantly. Even though THG depends more sensitively on the irradiance (cubic) than SHG (square), the THG intensity only drops moderately on the edge of the FOV because the Strehl ratio decreases slower for the 1580 nm than 790 nm over the FOV. Note that the Strehl ratio and the signal intensities plotted here are relative values and normalized to the maximum value within the FOV. In fact, THG can be orders of magnitude weaker than SHG because THG is a higher-order nonlinear effect. It can also be seen that with 790-nm wavelength, the full-width-half-maximum (FWHM) of the PSF is narrower than that of 1580-nm, showing two-photon imaging excited by the shorter wavelength has a higher resolution than three-photon imaging. Emitted signals from the focal volume in the sample are collected backward through the objective, and separated from the excitation beam path by a single-edge dichroic beam splitter (FF665-Di02, Semrock) with an edge wavelength of 665 nm. A multimode fiber with a diameter of 1.5 mm is employed to collect the signals. Another dichroic beam splitter (FF414-Di01, 52 Semrock) separates the 2PEF and SHG signals before reaching two PMTs. 2PEF and SHG signals are acquired simultaneously by PMT1 (H9305-03, Hamamatsu) and PMT2 (H6780-20, Hamamatsu), respectively, under the excitation of 790 nm. THG signal is acquired serially by PMT1, under the excitation of 1580 nm, after the two-photon imaging. Signals from the PMTs are delivered to two photon-counting units. A data acquisition (DAQ) board (PCIe-6363, National Instrument) is utilized to control the scanning of the MEMS mirror and to receive signals from the counting units. The DAQ module communicates with a PC through a PCIe slot. The PC runs a home-developed Visual C++ program that controls the DAQ module, communicates with the Arduino microcontroller (which controls the SMA actuator) through a serial port, and handles the image formation and storage. The controlling and signal flowchart is also shown in Figure 3.1. 3.3 SMA-based depth scanning Here we will explain the SMA-enabled depth scanning. Figure 3.4 demonstrates the engineering design and assembly of the SMA-actuated objective lens unit. The aspheric lens of the objective is attached to an SMA actuator (ASA10080-101, Actuator Solutions) which allows for a 370 μm translation of the lens. The plano-convex lens is kept stationary which ensures stable and safe positioning of tissue. Immersion water is applied between the plano-convex lens and the tissue surface to reduce sample-induced aberration. When focusing light from an objective lens into a sample, the refractive index of the lens immersion medium (e.g. air for the aspheric lens) is different from that of the sample, which causes light refraction at the interface. Thus, a correction factor is needed to convert the actuation of the aspheric lens to the translation of the focal plane inside the tissue [165]. The correction factor is measured experimentally to be ~1.2 by comparing the distance travelled by the aspheric lens with the distance moved by the sample plane controlled 53 by a piezo stage. The experimental measurement matches with the correction factor obtained by Zemax simulation. In Figure 3.4, the aspheric lens is mounted in a 3D-printed lens holder that is attached to the SMA actuator’s ring-shaped driving component. The SMA actuator is mounted in a 3D-printed objective chassis which also holds the plano-convex lens in place. All 3D-printed components are printed by a laser stereolithography printer (Form 3, Formlabs) with photopolymer resin (Clear V4, Formlabs) with a layer thickness setting of 25 μm. Mounted components are bonded using epoxy (EP) (DP490, 3M) after alignment. The physical dimension of the SMA actuator (including the mounted aspheric lens) and the whole objective unit are 10×10×4 mm3 and 12×12×7 mm3, respectively. Figure 3.4 Construction of the SMA-actuated objective lens pack. AC – actuator; AD – actuator driving component; LH – lens holder; AL – aspheric lens; CL – plano-convex lens; OC – objective chassis; EP – epoxy; double-headed arrows indicate moving parts and directions. 54 A laser Doppler vibrometer (LDV) (OFV-5000/OFV-551 with displacement decoder DD-200, Polytec) is utilized to record the actuation trajectory of the SMA actuator. Figure 3.5(a) shows a fast depth scanning over the maximum travel distance of 370 μm at a step size of 37 μm and 0.55 sec per step. It shows that the SMA can scan over a long travel range at a relatively fast speed while also maintaining a uniform actuation motion. Figure 3.5(b) shows the actuator movement with a large step size of 200 μm and a long cycle duration of 30 s, which shows that the actuator can perform large stepping and hold a stable position if a long integration time is needed. The precise and fast scanning of the SMA is enabled by a closed-loop control where the position information is fed back by a Hall sensor integrated inside the SMA actuator. Figure 3.5(c) shows the Hall sensor detected position versus the command position for a forward and backward scanning over a travel distance of 370 μm. The Hall sensor data indicates relatively high linearity and precision in the actuator positioning. Due to the closed-loop control, the hysteresis of the SMA is also significantly reduced. Based on the LDV movement test shown in Figure 3.5(a), the hysteresis is calculated to be ~0.86 μm within the full range of 370 μm. Based on the Hall sensor data shown in Figure 3.5(c), the hysteresis is calculated to be 0.21 μm for the same range of 370 μm. Stability is calculated to be 0.19 μm, which is defined as the root-mean-square deviation (RMSD) between the measured position and the command position for measuring the fluctuation over time. 55 Figure 3.5 Performance characterization of the SMA actuator. (a) LDV measurement of the actuator movement in the full range of 370 μm; (b) LDV measurement of the actuator movement with a large step size of 200 μm and a long cycle duration of 30 s; (c) Actuator position measured by Hall sensor vs. command position under closed-loop control. Figure 3.6(a) andFigure 3.6(b) show the response curve measured by the Hall sensor for forwarding and backward actuation, respectively, with different command travel distances. The Hall sensor has a dynamic sampling rate of up to 200 Hz. The measurement is taken with the lens mounted, as the weight influences the response time. For the full range of 370 μm, the response time (5%–95%) <150 ms for both actuating forward and backward. The response time reduces with the travel distance and for mid-to-small steps below 50 μm, the response time is within 20 ms. In comparison, the response time can also be obtained from the LDV data, and it is measured to be 148 and 24 ms for the 370- and 50-μm range, respectively. Table 3.1 summarizes the actuator 56 performance measured by the LDV and the Hall sensor, respectively. As we can see, both measurements show very consistent results. Compared with the ETL depth scanning used in previous research that shows a depth resolution over 30 μm [5], the SMA actuated aspheric lens provides higher resolution and higher linearity and precision in positioning. And compared with the piezoelectric solution in previous research that has a dimension of 22×17×10 mm3 plus the lens [90], our SMA-actuated scanner has a significantly smaller dimension which is important for clinical imaging probes. Table 3.1 Performance characterization of the SMA Actuator. Designed movement range (μm) 370 Full-range (370 μm) response time with lens mounted (ms) LDV measurement 148 Hall sensor measurement 150 Mid-range (50 μm) response time with lens mounted (ms) LDV measurement 24 Hall sensor measurement 20 Hysteresis (μm) LDV measurement 0.86 Hall sensor measurement 0.21 Dimension (lens included) (mm3) 10×10×4 Figure 3.6 Actuator response time measured with the lens mounted. (a) Actuating forward; (b) Actuating backward. 57 3.4 Characterization of field of view and resolution The FOV in the XY plane is related to the focal length of the objective, scan lens, and tube lens, as well as the scanning angle of the MEMS mirror. According to the geometric relationship between the components, it can be calculated by the following equation , (3.1) where fobj, fscan, and ftube are the focal lengths of the objective, scan lens, and tube lens, respectively; and θ is the maximum angle of the beam relative to the optical axis after the MEMS. Because the MEMS mirror scans fast in the horizontal direction, a sinusoidal driving signal is adopted for its horizontal scanning to avoid high-frequency components in a triangle wave or sawtooth wave under the Fourier transform. Maximum scanning angles in the horizontal and vertical directions are ±2.3° and ±3.5°, respectively. Given that fobj = 3.6 mm, fscan = 25 mm, and ftube =50 mm, the FOV can be calculated to be 145×220 μm2. Experimentally, the FOV in the XY plane is measured to be ~144×224 μm2 by imaging a stripe microscope target (200 lines/mm). The measured result is consistent with the calculated FOV. The system records 512×512 matrices as the raw data, and after correcting the non-uniformity from the horizontal sinusoidal scanning, the formed images show a rectangular FOV. The pixel resolution is 328×512 which is adequate, according to the Nyquist-Shannon sampling theorem [166], to present the images with the FOV of 144×224 μm2 under the XY resolution of 0.88 μm. Based on the assumption that the back aperture of the objective lens is uniformly illuminated and the focus is diffraction-limited, the theoretical limit of the resolution can be defined as following equations by calculating the illumination PSF [2,167]. scanobjtubetan2 fFOV ffq= 58 (3.2) (3.3) (3.4) (3.5) Where Rxy2P, Rz2P, Rxy3P, and Rz3P are the resolutions in the XY plane and in the axial (Z) direction for two-photon imaging and three-photon imaging, respectively. Here λ is the excitation wavelength and n is the refractive index of the medium. Given that NA = 0.53, λ = 0.79 μm for two-photon imaging, λ = 1.58 μm for three-photon imaging, and n = 1.333 for water, the theoretical resolution values can be calculated as Rxy2P = 0.56 μm, Rz2P = 4.50 μm, Rxy3P = 0.92 μm, and Rz3P = 7.35 μm. Experimentally, the PSF can be measured by imaging a point object [168–170]. The resolution of two-photon imaging is measured from fluorescent carboxylate microspheres with a diameter of ~0.1 μm (Fluoresbrite YG, Polysciences) embedded in an agarose phantom. The resolution of THG imaging is determined by measuring a waveguide wire (with a width and height of ~500 nm) on a silicon photonic chip. Figure 3.7 shows the experimentally measured FWHM resolutions of the MPM system. The XY and Z resolution of the 2PEF is measured to be 0.9 and 10.1 μm, respectively; and that of the THG imaging is measured to be 1.4 and 15.3 μm, respectively. Table 3.2 summarizes the theoretically calculated and experimentally measured FOV and resolutions of the MPM system. In both the XY plane and axial direction, the resolution of xy2P0.3202ln 2RNAl=xy3Pln 2 0.32023RNAl=z2P 2 20.5322ln 2Rn n NAl=- -z3P 2 2ln 2 0.53223Rn n NAl=- - 59 three-photon imaging is about 50% worse than that of two-photon imaging due to the twice longer excitation wavelength. Figure 3.7 Experimentally measured FWHM resolution of the MPM system. (a) 2PEF resolution in the XY plane; (b) 2PEF resolution in the Z (depth) direction; (c) THG resolution in the XY plane; (d) THG resolution in the Z (depth) direction. Table 3.2 Measured FOV and resolution of the MPM system. Excitation scheme FOV Plane resolution (μm) Theoretical Measured Theoretical Measured Two-photon (2PEF) 145×220 μm2 144×224 μm2 XY 0.6 0.9 Z 4.5 10.1 Three-photon (THG) XY 0.9 1.4 Z 7.4 15.3 60 The experimentally measured resolution has not reached the diffraction-limited resolution calculated by Eqs. (3.2)–(3.5). One major reason is that the theoretical calculation assumes the objective is uniformly illuminated (overfilled), but experimentally due to the small MEMS mirror, the excitation beam diameter is limited, and the back aperture of the objective lens is not fully illuminated under the 25-mm scan lens. To verify this, the scan lens is replaced by another lens with a focal length of fL3 = 16 mm. The 16-mm scan lens expands the beam diameter by ~1.6 times more than the 25-mm scan lens to illuminate the back aperture of the objective lens for a higher effective NA. Using the 16-mm scan lens, the XY and Z resolutions of two-photon imaging are improved to 0.7 and 8.9 μm, respectively; and those of the THG imaging are improved to 1.1 and 14.8 μm, respectively. This improves the resolution closer to the theoretical limit but at the cost of a reduced FOV as per Eq. (3.1). With the 16-mm scan lens, the FOV in the XY plane is measured to be ~93×145 μm2, which is reduced by ~58% compared with that by the 25-mm scan lens. Other possible factors for the difference between experimental and theoretical resolution include chromatic aberration and astigmatism. The excitation laser has relatively broad bandwidths of ~118 nm (–10 dB) at 1580 nm and ~30 nm (–10 dB) at 790 nm [164]. And since the objective is not designed to be achromatic, different wavelengths in this band can be focused on slightly different depths and making chromatic aberration a contributor to the resolution limitation. Astigmatism caused by alignment errors is also considered to be a potential factor that influences the resolution. Even though the current resolution is sufficient for resolving most tissue structures (e.g. cells), further improvement of resolution will have a significant impact on increasing SNR (that originates from a better focal point) and penetration depth which will improve the overall imaging quality. 61 3.5 Results and discussions To demonstrate the capability of multimodal MPM imaging with depth scanning, various animal tissue samples and plant samples are imaged. Three channels of contrast signals are acquired, where 2PEF and SHG signals are excited by 790-nm pulses and THG signal is excited by 1580-nm pulses. Depth scanning is achieved by actuating the objective and the penetration depth is limited by signal attenuation due to light absorption and scattering in tissue. The SMA-actuated objective acquires image stacks with a step size of 4 μm, and the results typically show ~250 μm penetration into soft tissues and ~150 μm penetration into the bone tissue. The laser power applied on samples is ~30 and ~60 mW for the 790- and 1580-nm wavelengths, respectively. There is a focal shift of ~80 μm between the two- and three-photon raw images due to the different excitation wavelengths, and it is corrected when merging the channels. The 2PEF, SHG, and THG channels are color-coded in red, green, and blue, respectively, in the combined multimodal images. Figure 3.8 shows the acquired images from animal tissues such as mouse ear pinna, fish skin, and mouse femur at different depths. The images show different layer structures on both the skin tissue and the bone tissue with complementary information from the three channels differentiating a variety of structures and substances inside. In Figure 3.8(a)–Figure 3.8(c), the epidermis cellular layer can be identified from the mouse ear pinna images as small honeycomb structures (diameter of ~8 μm) near the tissue surface. The dark center indicates the nucleus and 2PEF signals mainly come from the surrounding cytoplasm and intercellular substances. Large honeycomb structures (diameter of ~27 μm) can be observed starting from 70–90 μm depth with 2PEF and THG signals and those could be cartilage. SHG signals are also observed and they mainly come from collagen. In Figure 3.8(d), the fish skin shows SHG signals with a high intensity that indicates a high concentration of collagen fibers. Lipid-like circular structures showing 2PEF 62 signals in the center and THG signals on the boundary are observed between the depths of ~50–200 μm. In Figure 3.8(e) and Figure 3.8(f), the lacuna-canalicular framework can be observed from the mouse femur images. For the animal tissue images shown in Figure 3.8, the signal level and SNR of THG images are much lower compared with that of the two-photon images so the THG images are averaged over three frames to improve the SNR. Figure 3.9 shows the acquired images from fungus and plant tissues such as the lamella (grill) of Amanita muscaria (commonly known as fly agaric), lamina of Acer rubrum (commonly known as red maple) leaf, and leaf midrib. Fungus and plant samples in Figure 3.9 provide mainly 2PEF signals but with some important features differentiated by the THG channel. Unlike animal tissues which have abundant collagen fibers, only very weak SHG signals are obtained from fungus and plant tissues. Lamellae are the structures that contain spores of the mushroom [171,172], and in Figure 3.9(a), spores are differentiated from the lamella tissue by the THG signal and shown as round or oval-shaped structures in blue with diameters of around 6 to 10 μm. Mushroom spores show relatively strong THG signals and only THG signals under the described excitation wavelengths. While the background lamella tissue shows mainly 2PEF signals, the different structures are effectively differentiated by the multimodal capability of the MPM system. Stomata of the Acer rubrum leaf can be seen in Figure 3.9(b) with strip structures underneath showing THG signals in part of the FOV. In Figure 3.9(c), cell walls showing 2PEF signals as well as some subcellular features can be clearly seen from the leaf midrib. Honeycomb structures can be identified from deeper layers with various detailed structures inside showing 2PEF, SHG, and THG signals. THG signals from the fungus and leaves are stronger compared with that from some of the animal tissues and the images shown in Figure 3.9 are not averaged over frames. 63 Figure 3.8 Multimodal MPM images from animal tissues at different depths. (a)–(c) Mouse ear pinna; (d) fish skin; (e) and (f) mouse femur. THG images are averaged over three frames; 2PEF and SHG images are not averaged. Red – 2PEF; Green – SHG; Blue – THG. Scale bar is 50 μm. 64 The SNR decreases as the imaging plane gets deeper into the tissue, and images are typically blurred out when the penetration depth exceeds 250 μm for animal soft tissues or 150 μm for bone, fungus, or leaf tissues. Thanks to the custom water-dipping objective that minimizes spherical aberration, this penetration depth is approximately two times deeper than the result from a single aspheric lens working in the air. Under the circumstance of no tissue clearing processes such as CUBIC and 3DISCO, our achieved penetration is deeper compared with the penetration depths of 64–120 μm reported in recent studies of depth scanning MPM systems [5,90,107]. Scattering of the excitation beam by the tissue is considered to be one major reason that causes the quality of the focal point deep inside to deteriorate and thus, limits the penetration depth. For the THG excitation, water absorption is another cause that could limit laser penetration, as the absorption coefficient for water around the wavelength of ~1500 nm reaches ~20 cm−1, which is significantly higher than that of 0.02 cm−1 around ~800 nm [132,133,173]. In future studies, a 1580-nm laser with a higher power and shorter pulsewidth may be employed to improve the THG image quality for animal tissue imaging. However, the higher laser power comes at the cost of increased thermal damage risks, and shorter pulsewidth will lead to broader bandwidth which can worsen the chromatic aberration. This is a trade-off that needs to be considered during optical design. Lowering the repetition rate in exchange for higher peak power is also likely beneficial for THG excitation [174], and it can be a potential approach for future improvements. Optics that are optimized for both 790 nm and 1580 nm wavelengths may potentially be developed in future work to further help address the limitation of THG signal strength. As reported in several studies [175,176], adaptive optics may potentially be used to correct aberrations from the microscope’s internal optics, the objective, and also those induced by the tissue, thus further increasing resolution and imaging depth. 65 Figure 3.9 Multimodal MPM images from fungus and plant tissue samples at different depths. (a) Lamella (grill) of Amanita muscaria (fly agaric) mushroom; (b) lamina of Acer rubrum (red maple) leaf; (c) leaf midrib. All 2PEF, SHG, and THG images are not averaged. Red – 2PEF; Green – SHG; Blue – THG. Scale bar is 50 μm. 3.6 Conclusion In this chapter, a fiber-laser-based compact multimodal MPM system with depth-scanning capability has been developed and demonstrated. Our home-built Er-doped femtosecond fiber laser with PPLN provides excitation wavelengths of 1580 and 790 nm for three-photon and two-photon multimodal imaging, respectively. A custom-designed miniaturized objective incorporates an SMA actuator for depth scanning. Multimodal image stacks containing 2PEF, SHG, and THG channels have been acquired from animal, fungus, and plant tissues. The image stacks cover the depth range of over 250 μm for animal soft tissues and of 150 μm for animal bone, fungus, and plant tissues. Complementary and comprehensive information is obtained label-free due to the 66 multimodal and depth-scanning capabilities. A variety of structures such as the epidermis cellular layer and collagen fibers, the lacuna-canalicular framework in bone tissue, spores of fungi, and the stomata, cell wall, and subcellular features from leaves can be differentiated by multimodal imaging. Our miniaturized multimodal MPM with the depth-scanning objective shows great potential in future MPM probe and endoscope designs for clinical use. 67 Chapter 4: Kernel-based nonlinear scaling denoising method for low-signal MPM images To improve the quality of low-signal label-free MPM images and to visualize weak-signal channels in merged multimodal images, a denoising method is developed in this chapter. This denoising method can significantly improve the SNR of low-signal MPM images with low requirements for computing power which realizes video-rate denoising for potential clinical real-time imaging. 4.1 Introduction In label-free multimodal MPM imaging, apart from the challenges in the optical design, the data processing of combined 2PM and 3PM imaging also has its unique challenges. While the different contrasts (e.g. 2PEF, SHG, and THG) visualize complementary tissue information, the signal level for each contrast can vary significantly, giving different detection channels highly differentiated SNRs. This renders it difficult to visualize weak-signal channels, especially in merged multimodal images. For example, 3PM signals can be orders of magnitude weaker than 2PM signals [177]. Meanwhile, THG images from 3PM can reveal important information on tissue structures that lack 2PM contrasts, such as aqueous-lipidic interfaces in animal tissues and spores in plant tissues [65,66,178]. As another example, certain samples may only generate very weak (but still meaningful) signals on certain contrasts due to their structural and/or biochemical properties [66]. To reduce the amount of noise that would be introduced to the merged multimodal MPM images, images from the low-signal channels require denoising and SNR improvement before being merged with other channels. 68 Traditional denoising methods such as outlier removal and median filters are usually not effective in removing noise from those low-signal images while preserving tissue feature patterns [179]. In recent years, machine learning methods, especially deep convolutional neural network (CNN) algorithms, have been demonstrated for denoising general images and showing better results compared to traditional methods [180,181]. Machine learning denoising has also been utilized to denoise MPM images. Niu et al. demonstrated a neural-network-based method that effectively reduced noise from THG and 3PEF images of Hoechst blue labeled RAW 264.7 cells [180]. However, the feasibility of such methods typically relies on the availability of abundant training data and ground truth [182]. In addition, the effectiveness of the trained neural network model may typically be limited to images with signal patterns similar to the training data. As training datasets are not always available in tissue imaging, M. Weigert et al. proposed a method in which the training can be done with synthetic data instead of real microscopy images [183]. With their content-aware image restoration (CARE) networks, significant improvement in fluorescence images is demonstrated even using synthetic training data. However, there are still concerns regarding the reliability. In particular, when the network is applied to images that contain biological structures absent from the training set, the results are likely to suffer in quality [183]. Especially with synthetic data, it can be difficult to include all possible (known and unknown) structural features in the training images. Ground truth is another prerequisite that can be unavailable in tissue imaging. In recent years, self-supervised machine-learning methods have been developed to enable training using datasets without ground truth [184]. Noise2Void (N2V) is a widely used self-supervised denoising algorithm that allows training using a single image without ground truth [185,186]. Nevertheless, machine-learning methods have the well- 69 known black box problem, meaning that the trained model is typically not interpretable, which renders it difficult to explain how the image is processed [187]. In this chapter, we will address the data processing challenges related to multimodal imaging. Specifically, a kernel-based denoise and contrast enhancement method is introduced and demonstrated for improving images from low-signal channels and forming clean multimodal images. Comparisons are conducted between our denoising method and several other approaches including common traditional methods and N2V. The reason for selecting N2V for comparison is that it does not require large training data sets or ground truth. When prerequisites such as large training data sets, ground truth, sufficient computing power, and training/processing time are available, there are various machine learning methods that can achieve satisfactory denoising results. However, large training data sets or ground truth are not available in many cases in tissue imaging. Our method does not aim to compete with or outperform machine learning methods in general cases. Rather, we propose the simple, fast, but effective method dedicated to low-signal MPM images that provides a solution for the cases when 1) large training datasets and ground truth are not available, or 2) fast processing is needed (e.g. video-rate real-time imaging). Therefore, as a self-supervised denoising method with reputable performance, N2V is suitable for comparison in this chapter. Lastly, combined 2PM and 3PM images are demonstrated with weak channels denoised by KNS. 4.2 Denoising algorithm To address the challenge of the large variation in the SNR among the multimodal images and to visualize weak-signal channels in merged multimodal images, we propose a kernel-based nonlinear scaling (KNS) denoising algorithm. Some preliminary results of the KNS algorithm were 70 reported in our previous publication Ref. [66]. Here we will explain the KNS algorithm in detail, and provide improved results and more comprehensive comparisons with various denoising approaches. The principle of this denoising algorithm is to adjust pixel brightness (intensity) based on a comparison between the global average brightness of the image and the local average brightness of the targeted pixel’s surrounding kernel. Based on the main tissue features (e.g., epidermis cells, osteocytes, Volkmann’s canals) shown in the image, a square-shaped kernel size of nK × nK pixels is empirically chosen to be ~50–100% of the tissue feature size in the image (that may further be related to the resolution). For each pixel (x, y) on an image with a dimension of X × Y pixels, a kernel centered on the pixel (x, y) with a dimension of nK × nK is then generated. The relationship between the scaled brightness Is and the original brightness Io of pixel (x, y) is defined as , (4.1) where R (x, y) is a scaling ratio that depends on both the local and global average brightness. In general, R (x, y) is higher when the local average to global average ratio is lower. To determine the scaling ratios R for different quotients of global average and local average, scattered scaling ratio data are first manually tuned on testing low-signal MPM images to reach an optimum denoising result (noise removal and signal preservation) through trial and error. In the manual tuning process, samples with known structures are imaged and the effectiveness of denoising is empirically judged by how well the background noise is reduced and how well the tissue structures are preserved. A lookup table between R and quotients of global average and local average is then established based on the manually tuned scaling ratios. Afterward, an analytical expression of R ( ) ( )( )os,,,I x yI x yR x y= 71 (x, y) is obtained by exponential curve fitting of the scattered data in the lookup table. For the MPM images we have processed, R (x, y) can be obtained using the following formula, (4.2) Here IGM is the global mean intensity of the entire image, and IKM (x, y) is the local mean intensity within the kernel centered on the pixel (x, y). IGM and IKM (x, y) are respectively calculated as , (4.3) , (4.4) where xK and yK are pixel coordinates within the kernel. This brightness scaling is performed on every pixel of the image. In case the targeted pixel is close to the edge of the image that causes the kernel (centered on that pixel) with the pre-selected dimension to exceed the image boundary, the actual effective kernel will be cropped by the image boundary to a reduced dimension. 4.3 Verification of the denoising method Figure 4.1 shows representative images of low-SNR denoised by KNS and various commonly used denoising methods. The raw images include two THG images of mouse ear pinnae, an SHG image of the lamina of an Acer rubrum (red maple) leaf, and a THG image of a mouse femur bone. In our previous work [66], we demonstrated a comparison between an early-version KNS (the scaling ratio was based on a lookup table, and KNS has no iteration) and outlier removal using the mouse pinna and maple lamina images. Here in Figure 4.1, additional denoising methods such as the ( ) ( ) ( )GM GMKM KM0.94 0.59, ,, 0.1 0.75I II x y I x yR x y e e= +( )GM o1 ,X YI I x yXY= åå( ) ( )K KKM o K K2K1, ,n nI x y I x yn= åå 72 Gaussian filter and bounded-variable least squares (BVLS) deconvolution algorithm have been added for comparison [188]. Furthermore, KNS is improved to be based on the empirical analytical scaling function Eq. (4.2) and be able to operate with multiple iterations. To show a more realistic SNR in generally dark images with few scattered signals, we also updated the definition of signal in SNR calculation from the global average brightness in Ref. [66] to the highest local average brightness. In this study, SNR is calculated by taking the ratio between the highest local average and the lowest local average, where the local averages are calculated for each image block with 20 × 20 pixels. The THG contrast of mouse pinna shows the cartilage layer, where the large (diameter of ~30 μm) honeycomb structures could originate from the interfaces created by chondrocyte lacunae [189]. The SHG contrast in the maple lamina could be generated from starch in chloroplasts and cellulose in cell walls [190,191]. The THG image of mouse femur bone shows oval-shaped structures that indicate the location of osteocytes. The THG contrast could originate from the interfaces created by the osteocyte lacunae which are part of the lacuna-canalicular network. All the images contain 328 × 512 pixels. For KNS denoising, the kernel size is selected to be 20 × 20 pixels. KNS is operated on Matlab and the other methods are operated on ImageJ. As shown in Figure 4.1, KNS effectively reduces noise on those low-signal images and can result in an SNR increase of more than 10 dB without significant loss of feature details. In comparison, commonly used traditional methods such as outlier removal, Gaussian filter, and BVLS fail to provide a noticeable SNR improvement to those low-signal MPM images. Thus, KNS outperforms those traditional methods. KNS is able to preserve the brightness of tissue features while reducing the background noise. Most tissue features such as the honeycomb structures in the mouse pinna are well-preserved, and the contrast of tissue features such as circular structures on the leaf lamina is increased. KNS can also be applied iteratively. In Figure 4.1, KNS 73 results with iteration numbers N = 1 and 2 are shown. Iteratively applying KNS may exponentially increase the SNR, as shown in Figure 4.1. However, increasing the iteration number can result in a loss of image details. Figure 4.1 Comparison of MPM images before and after denoising by outlier removal, Gaussian filter, BVLS deconvolution and KNS. SNR is calculated and shown in each image. σ – radius of the Gaussian filter; N – numbers of iteration. Cyan squares on KNS denoised images indicate the size of the kernel. Scale bar is 40 μm. Figure 4.2 compares the denoising results obtained by KNS (with iteration number N = 1 or 2) and N2V. N2V is a self-supervised deep-learning method that can be trained by a single image without ground truth [185,186]. N2V training and prediction are operated using the 74 CSBDeep-N2V plugin of ImageJ [185,192]. The N2V model is trained by one MPM image and then applied to three other low-signal MPM images as well as the training image itself. In N2V training, the number of epochs, number of steps per epoch, batch size per step, patch shape, and neighborhood radius are set to be 300, 200, 56, 64, and 5, respectively. The KNS method is applied to the same images for comparison. The N2V model in Figure 4.2(a) is trained by a human bone 2PEF image with a relatively high signal level. The image shows osteocytes and their processes with fluorescence from endogenous fluorophores like NADH in the cytoplasm. The denoising result shows that except for the training image, KNS outperforms N2V in all three low-signal testing images. On the human bone image (also the training image), N2V shows better results as certain details including some canaliculi are dimmed or lost in the KNS results. In Figure 4.2(b), the N2V model is trained by a mouse pinna THG image which has an ultra-low signal level. The training does not show a typical successful process as it can be seen that while the training loss decreases through the training process, the validation loss increases. As a result, models from the last epoch and from the lowest validation loss show significantly different performances, and the last-epoch model fails to denoise images. The lowest-loss model is able to preserve some features but also introduces bright background artifacts. For all four images, KNS outperforms the N2V model in removing the noise while preserving tissue features. On the computation side, both methods ran on a computer equipped with an Intel i9 11900F CPU and an Nvidia RTX 3080 GPU. The N2V model costed ~21.5 hours to train and took ~1 s for each prediction; KNS took ~34 ms per iteration for each image. While KNS cannot be generally expected to outperform deep-learning methods such as N2V, in this comparison, KNS shows its specialty in processing images with ultra-low signal levels. Moreover, its simplicity enables fast computation and makes video-rate real-time processing possible. 75 Figure 4.2 Comparison between KNS and N2V in processing low-signal noisy images. The N2V models used in (a) and (b) are trained by (a1) a human bone 2PEF image with a relatively high SNR and (b1) a mouse pinna THG image with an ultra-low SNR, respectively. (a2) and (b2) show the training and validation loss through the training of these two models, respectively. 76 To further evaluate the denoising performance, we also compare the peak signal-to-noise ratio (PSNR) and the structural similarity index (SSIM) using simulated data. In this simulation, a real image with high SNR is used as the ground truth (reference image); additive Gaussian noise is added to the ground truth to synthesize a noisy input image; KNS and N2V methods are then applied to the noisy input image; finally, PSNR and SSIM are computed between the denoised image and the ground truth. Figure 4.3 shows the results of this comparison. A relatively clean THG image of Amanita muscaria (fly agaric mushroom) spores shown in Figure 4.3(a) is used as the ground truth. In Figure 4.3(b), Gaussian noise with a mean of zero and a standard deviation of 35 is added to the reference image to synthesize a noisy image which is then inputted to the denoising algorithms. Figure 4.3(c) shows the KNS denoising result with iteration number N = 2. Figure 4.3(d) and Figure 4.3(e) are the denoising results using the N2V model shown in Figure 4.2(a) and another N2V model trained by the input image Figure 4.3(b), respectively. Each image is calculated for its PSNR and SSIM with reference to the ground truth. The PSNR and SSIM calculations are performed by the SNR plugin [193] and SSIM index plugin [194] of ImageJ, respectively. The input noisy image has a PSNR and SSIM of 17.51 dB and 0.251, respectively, compared to the ground truth. After denoising by KNS, the PSNR and SSIM improved to 23.54 dB and 0.459, respectively. In contrast, the N2V processed two results reached only slightly improved PSNRs (18.06 and 18.11 dB) and even deteriorated SSIMs (0.220 and 0.190). In addition, the two N2V models resulted in observably different results. Compared with the model trained by the bone sample with a good SNR (shown in Figure 4.2(a)), the model trained by the input image (Figure 4.3(b)) gives a much-blurred prediction (Figure 4.3(e) compared with Figure 4.3(d)). This shows that because of the black-box effect of machine-learning-based methods, they may generate inconsistent and unpredictable results due to different training and/or input data. 77 Figure 4.3 Comparison of PSNR and SSIM obtained by KNS and N2V. (a) Ground truth (reference image). (b) Noisy input image created by adding Gaussian noise with a standard deviation of 35 onto (a). (c) KNS (iteration N = 2) denoised result. (d) Denoised result by an N2V model trained by a relatively high SNR image (shown in Figure 4.2(a)). (e) Denoised result by an N2V model trained by the noisy input image (b) with relatively low SNR. Denoising is particularly important in multimodal imaging. The multiple contrast images may have very different SNRs which can make the merged image very noisy or the weak channel(s) hard to distinguish. Figure 4.4 shows the contribution of the KNS denoising algorithm to the merging of multimodal images. A mouse pinna and a mouse femur are imaged by 2PEF, SHG, and THG. The THG signal is much weaker than the 2PEF and SHG signals. Without denoising, the noise level is close to the signal level on the THG images, and a large amount of noise is introduced to the merged images, affecting the readability of the other channels. In comparison, the KNS denoising algorithm effectively reduced the noise in the THG images. The merged images show significant improvement with tissue features well preserved and presented. During the denoising comparison with various tissue samples, the denoising algorithm appeared to perform stably under the selected kernel size (20 × 20 pixels). 78 Figure 4.4 Comparison of merging multimodal MPM images with and without KNS denoising on the low-signal THG channel. The gray and blue arrows indicate examples of noise and tissue features, respectively. 4.4 Conclusion To improve the image quality of low-signal channels and help form clean merged images, the KNS denoising method has been developed. With kernel size chosen by tissue feature dimensions, a kernel is generated centered on each pixel on an image, and the brightness of the central pixel is scaled by a nonlinear equation with variables based on the global and local (kernel) averages. The denoising method is generally universal to different tissue features and has been demonstrated on low-signal THG and SHG images acquired from animal and plant tissue samples. Comparisons between various denoising methods including outlier removal, Gaussian filter, deconvolution, and N2V have been conducted to verify the denoising performance. KNS outperforms the above-mentioned denoising methods on low-signal MPM images. The reason for KNS to work is that the method judges a pixel’s possibility to be noise by its surrounding brightness rather than the pixel 79 itself. This idea provides KNS the capability to distinguish signal and noise from images with signal intensities that are so low that even the part showing signals consists of highly sparkled pixels. As a result, KNS is particularly effective in processing ultra-low signal MPM images. However, for input images already with a relatively high SNR, KNS may cause artifacts that dim some signals. As a scaling method, KNS can alter the relative brightness between different tissue features shown on an image and may not be suitable for quantitative analyses. This effect can be more obvious on bright images with most parts showing signals rather than background noise. With the increased number of iterations, KNS may also cause a loss of image details, saturations, and gridded artifacts. Overall, in the merging of MPM images consisting of multiple channels with highly inconsistent SNRs, KNS improves the image quality by significantly reducing the excessive amount of noise introduced to the superposed image from low-signal noisy channels. KNS provides an effective and fast denoising approach for low-signal images whenever ground truth images are not available. 80 Chapter 5: Simultaneous two-photon and three-photon microscopy for label-free multimodal MPM imaging This chapter introduces a method to realize simultaneous excitation and acquisition of two- and three-photon multimodal MPM imaging. With the efforts of miniaturization presented in previous chapters taken into use, a compact multimodal MPM with simultaneous 2PEF, SHG, and THG imaging and depth-scanning is demonstrated on animal, plant, and human tissues. The system shows great potential in future clinical applications. 5.1 Introduction Multiple contrasts acquired by various nonlinear optical effects make MPM a powerful and widely utilized technique for non-invasive and label-free imaging of biological tissues [5,109,195–200]. In two-photon microscopy (2PM), 2PEF can be acquired from intrinsic fluorophores such as NADH in cells, and SHG can be acquired from non-central symmetric molecules such as collagen fibers; in three-photon microscopy (3PM), 3PEF can also be acquired from intrinsic fluorophores, and THG can be acquired from interfaces such as the lipid aqueous medium interface [6,52,61,99,112,114,159]. As demonstrated in Chapters 2 and 3, being able to acquire multiple contrasts is important in label-free imaging because it can provide a comprehensive understanding of tissue morphology and function. With the advancement in lasers as well as signal detection techniques, combining 2PM and 3PM in a single system has gained a lot of interest as it can provide complementary information about tissues including cells, collagen fibers, lipids, etc., and form information-rich images that are essential for label-free tissue imaging [66,101,110,111,178,201]. 81 Several groups have demonstrated combined 2PM and 3PM imaging systems. F. Akhoundi et al. demonstrated a 2PM (SHG) and 3PM (3PEF and THG) probe using a single excitation wavelength at 1700 nm [88]. However, their THG and 3PEF signals were acquired sequentially by a PMT, and samples were hematoxylin and eosin (H&E) stained for 3PEF imaging. At 1700-nm excitation wavelength, it is not possible to excite 2PEF from tissues without staining, and 3PEF is a higher-order nonlinear effect with much lower efficiency than 2PEF. Although excitation by a single wavelength is simple, it has many limitations in combined 2PM and 3PM imaging. On one hand, lasers with long wavelengths (>1 μm) cannot efficiently excite 2PEF from intrinsic fluorophores in tissues [70,113]. On the other hand, a short excitation wavelength (<1 μm) is not optimal for THG imaging as the signal wavelength will fall inside the ultraviolet regime [70]. To achieve both 2PM and 3PM from tissues label-free, several other groups including our group have used two excitation wavelengths, where a shorter wavelength (<1 μm) is for 2PM and a longer wavelength (>1 μm) for 3PM, respectively [66,98,101,113,202,203]. D. Septier et al. demonstrated 2PM (SHG) and 3PM (3PEF and THG) in mouse skin excited by 920 and 1300 nm, respectively [202]. By using a single PMT, all three images were acquired one after another. F. Aptel et al. performed 2PM (2PEF and SHG) and 3PM (THG) imaging of human cornea using dual excitation wavelengths [203], where a 730-nm wavelength excited 2PEF and a 1200-nm wavelength excited SHG and THG. The two wavelengths were applied on tissue in sequential mode. Previously, our group has demonstrated combined 2PM (2PEF and SHG) and 3PM (THG) using dual excitation wavelengths at 790 and 1580 nm obtained from a frequency-doubled erbium-doped fiber laser [66,101,138]. 3D volumetric 2PM and 3PM image stacks were acquired in sequential mode, where the 790- and 1580-nm excitation wavelengths were applied to tissue one after another. There are several drawbacks to the sequential acquisition of 2PM and 3PM images. 82 First, it doubles the image acquisition time because the 790- and 1580-nm excitation wavelengths are applied one after another. Second, it is vulnerable to motion artifacts and mechanical drifts which can cause spatial mismatch when merging the 2PM and 3PM images. The acquisition of corresponding frames in 2PM and 3PM stacks are separated by the duration of imaging a whole stack which can last several minutes depending on the number of frames in a stack [66]. Meanwhile, motion artifacts are especially critical during in-vivo imaging. To reduce motion artifacts and mechanical drifts in the sequential acquisition of 2PM (2PEF and SHG) and 3PM (THG) imaging, A. Filippi et al. used computer-controlled mechanical shutters to alternate wavelengths for 2PM and 3PM between consecutive frame acquisition [163]. The 2PEF and THG images were excited by wavelengths of 800 and 1200 nm, respectively, and the SHG images were excited by both wavelengths [163]. The frame-level interleaving mitigated inconsistency caused by sample motion or mechanical drift between 2PM and 3PM images. However, for each frame (depth) in a stack, the 2PM and 3PM images were still acquired sequentially, and the total imaging time would still be doubled compared with simultaneous acquisition. Temporal multiplexing can further improve the simultaneous acquisition of 2PM and 3PM signals at the pixel level by interleaving 2PM and 3PM excitation pulses [204,205]. D. G. Ouzounov et al. interleaved 920-nm pulses from a Ti:sapphire laser modulated with an electro-optical modulator with 1300-nm pulses from a noncollinear optical parametric amplifier (400-kHz repetition rate) in 2.5-μs periods, respectively [204]. They performed simultaneous recording of neuron activity with 2PEF and 3PEF on GCaMP6-labeled neurons at the same site and time in order to validate functional imaging of 3PM by comparing it with 2PM [204]. The temporal multiplexing approach allowed the simultaneous acquisition of 2PM and 3PM signals within μs 83 time delay. In order to increase the imaging volume in neuroscience study, S. Weisenburger et al. utilized spatiotemporal multiplexing of 2PM and 3PM to image multiple sub-volumes at the same time [205]. A custom laser system outputted 960 nm for 2PM and 1300 nm for 3PM excitation, at repetition rates of 4.7 and 1.0 MHz, respectively. They interleaved four 960-nm pulses and one 1300-nm pulse, each delayed by 8 ns through optical delay lines, forming five beamlets, each targeting a different sub-volume. Specifically, the 1300-nm beamlet was used to target a deeper axial location due to its higher penetration depth. Temporal multiplexing provides an alternative approach for signals that cannot be separated in the spectral domain. In both Refs. [204,205], 2PEF and 3PEF signals were acquired from GCaMP6-labeled neurons where the fluorescence emission signals could not be separately spectrally. However, temporal multiplexing generally requires high system complexity due to the requirements on modulators, optical delay lines, precise timing, and synchronization with the detection unit. In this chapter, we will address the challenges of applying two excitation wavelengths on tissue simultaneously for multimodal 2PM and 3PM imaging. We are aiming for a compact fiber-based system design and all label-free imaging. When two excitation wavelengths are shone on the sample simultaneously, the separation of 2PM and 3PM signals becomes difficult due to a partial wavelength overlapping between 2PEF and THG. Other challenges include the risk of thermal damage due to the additional excitation beam, and the difficulty to align two beams accurately for imaging the same field of view if two separate laser sources are utilized. Finally, combined 2PM and 3PM imaging which simultaneously acquires 2PEF, SHG, and THG contrasts is demonstrated on biological samples, and the multimodal images are processed by our proposed denoising method to show the potential of this compact multimodal MPM system. 84 5.2 System setup To perform simultaneous 2PM (2PEF and SHG) and 3PM (THG) imaging, a dual-wavelength excitation approach is used, where excitation wavelengths of ~790 nm and ~1580 nm are obtained from a frequency-doubled erbium-doped fiber laser with a pulse width of ~80 fs and a repetition rate at 50 MHz. Our laser system enables fiber-delivery of femtosecond pulses, which brings about advantages in compactness and portability for a potentially handheld imaging head. With the frequency-doubling scheme, both 2PM and 3PM can be achieved with the single laser source and that further enhances the compactness. Figure 5.1 shows the optical layout of the combined 2PM and 3PM imaging system. The 1580-nm laser is partially frequency-doubled to 790 nm by a PPLN crystal (MSHG1550-0.5-0.3, Covesion). The maximum conversion efficiency for the PPLN is 35%. However, as THG can be 10 times weaker than 2PM signals [177], the PPLN is placed slightly out of focus to preserve a larger portion of 1580 power for THG excitation. Thus, the conversion efficiency is controlled at ~18%. The laser power applied on samples is ~9 and ~40 mW at wavelengths of 790 and 1580 nm, respectively. The PPLN crystal also generates a small amount of third harmonic (~527 nm, visible green). A long pass filter (FELH0700, Thorlabs) is utilized to remove the PPLN-generated third harmonic light from the excitation beam. The 790- and 1580-nm wavelengths are used to excite 2PM and 3PM signals, respectively. Compared with using two separate laser sources, the frequency-doubling solution avoids the difficulty of accurately aligning two beams since the fundamental and frequency-doubled beams are naturally overlapped. A miniaturized MEMS mirror (13Z2.1-2400, Mirrorcle Technologies) performs XY scanning of the excitation beams. The pixel dwell time is set at 10 μs and that gives a frame rate of ~0.4 FPS. The miniaturized objective is custom-designed and consists of an aspheric lens (352080, LightPath) and a plano-convex lens 85 (45469, Edmund Optics) [66,139]. The objective has a focal length of fOBJ = 3.6 mm and an NA of ~0.5. The aspheric lens is mounted inside an SMA actuator which enables axial scanning. Depth-resolved image stacks are acquired by scanning the aspheric lens in the depth direction (z). The lateral and depth resolutions for 2PM imaging are ~0.7 and ~9 μm, respectively; and those for 3PM imaging are ~1 and ~15 μm, respectively. Figure 5.1 Optical layout of the compact multimodal MPM imaging system with the illustration of the spectral relationship of the excitation and emission signals. L – lens; PPLN – periodically poled MgO:LiNbO3; MEMS – micro-electromechanical system; OBJ – objective lens pack; MMF – multimode fiber; DBS – dichroic beam splitter; PMT – photomultiplier tube; λe – excitation wavelength. The emitted 2PM and 3PM signals from the sample are collected backward through the same objective and are separated from the excitation beams by a long-pass dichroic beam splitter DBS1 (FF665-Di02, Semrock) with an edge wavelength of 665 nm. Signals are then coupled into a multimode fiber and delivered to the detection unit. Before being detected by PMTs, the multimodal signals are separated by two additional dichroic beam splitters DBS2 and DBS3. DBS2 86 is a long-pass dichroic beam splitter (FF414-Di01, Semrock) with an edge wavelength of 414 nm. SHG signal is reflected by DBS2 and then detected by PMT1 (H6780-20, Hamamatsu). The longer-wavelength lights containing 2PEF and THG signals are further separated by a notch dichroic beam splitter DBS3 (ZT532dcrb, Chroma). DBS3 has a central reflection wavelength of 532 nm and a FWHM notch bandwidth of 25 nm. THG signal is reflected by DBS3 and fed into PMT3 (H7422, Hamamatsu). As shown in the spectral relationship in Figure 5.1, due to a partial spectral overlapping between 2PEF and THG, a small portion of the 2PEF signal within the 532/25 nm band is also fed into PMT3. Thus, PMT3 contains THG + 2PEF signals. Most part of the 2PEF signal passes through DBS3 and then reaches PMT2 (H9305-03, Hamamatsu). The spectral relationship of the two excitation wavelengths and the three nonlinear signals of 2PEF, SHG, and THG are illustrated in Figure 5.1. A spectrograph (SP-2150i, Princeton Instruments) and an EMCCD camera (ProEM 512, Princeton Instruments) are used to measure and verify the spectrum of the emitted signals. The multimodal MPM system in Figure 5.1 contains multiple considerations for a compact design, including the fiber-delivered excitation laser and fiber-based signal collection, the miniaturized MEMS scanner and SMA actuator for XY and Z scanning, respectively, and the miniaturized objective with custom two-lens design. In the following sections, ex-vivo samples are imaged to demonstrate the proof of principle. Nevertheless, the compact design of this system will have the potential for in-vivo imaging. 5.3 Simultaneous acquisition of 2PM and 3PM signals One challenge in the simultaneous dual-wavelength excitation comes from chromatic aberration of the objective lens between the two excitation wavelengths. For a compact design, the objective 87 consists of a miniature aspheric lens and a plano-convex lens. While this lens design minimizes spherical aberration, there is still obvious chromatic aberration between the wavelengths of 790 and 1580 nm. Specifically, the 790- and 1580-nm wavelengths are focused to two different focal depths with a focal shift of ~80 μm as shown in Figure 5.2(a). The amount of focal shift is lens-specific, and it is calculated by Zemax simulation and verified by experimental calibration using a silicon photonic chip that contains structures with a thickness of hundreds of nanometers. Therefore, the acquired 2PM and 3PM image stacks would have a ~80 μm depth offset, which can be compensated in post-processing by shifting the THG image stack downward by 80 μm. Figure 5.2(b) shows the verification of 2PM and 3PM signal spectra using the photonic chip sample. When the 790 nm is focused on the photonic chip, the SHG peak and the broadband 2PEF emission are detected in the spectrum; when the 1580 nm is focused on the photonic chip, the THG peak is observed in the spectrum. Another challenge in simultaneous 2PM and 3PM imaging is the separation and simultaneous detection of multimodal signals. Based on the spectral relationship of the two excitation wavelengths and the 2PEF, SHG, and THG signals, dichroic beam splitters are carefully selected to spectrally separate the 2PEF, SHG, and THG signals. However, due to the partial spectral overlap between 2PEF and THG signals, PMT1, PMT2, and PMT3 detect SHG, 2PEF, and THG + 2PEF, respectively. Samples with both 2PEF and THG signals presenting in a considerable depth range may cause crosstalk from the 2PEF channel to the THG channel. Thin samples like the photonic chip are not significantly affected by this crosstalk issue. However, as shown in Figure 5.2(c), the problem starts to appear when the sample thickness and imaging penetration reaches the focal shift of ~80 μm. The relatively low depth resolution of the miniature objective may cause the excitation of out-of-focus signals which can add difficulty to the 88 separation of the THG signal. To obtain a clean THG image stack, an image post-processing method is proposed to remove the leaked 2PEF signal from THG images by performing a subtraction of ITHG = IPMT3 – r × IPMT2 [206]. Here ITHG is the signal intensity of the THG image, and IPMT3 and IPMT2 stand for the signal intensity received by PMT3 and PMT2, respectively. In the equation of subtraction, the intensity of PMT2 is multiplied by a correction factor r because the two channels use different PMTs with different gains which can lead to different brightness. A pre-calibration can be carried out to find the approximate value of r for potential real-time processing during imaging. However, as 2PEF signals are broadband with possible different spectra from different samples, the leaked portion of 2PEF signals can vary with samples and thus the optimal value of r may still need to be tuned in post-processing. The simultaneous acquisition of 2PM and 3PM signals doubles the imaging speed compared to sequential acquisition. Therefore, it can effectively reduce motion artifacts and mechanical drift between the 2PM and 3PM stacks. Figure 5.3(a) andFigure 5.3(b) show the superposed SHG and THG images of a mouse femur bone acquired sequentially and simultaneously, respectively. The images are from stacks with 90 frames and acquired at a frame rate of 0.4 FPS. In the sequential acquisition, a relatively long time delay of ~5 minutes exists between the 2PM and 3PM images from the same depth. The delay includes time for stack acquisition, manual wavelength switch, and saving the files. This long separation in acquisition time makes the imaging vulnerable to mechanical drift. In the current design of our compact MPM system, the sample is mounted vertically during imaging, and immersion water is added between the objective and the sample. Gravity pulling on the sample and immersion water can cause mechanical drift. Mechanical drift can also originate from tension and elastic deformation in tissues. In Figure 5.3, SHG contrast comes from collagen fibers while the locations of osteocytes 89 lack SHG signal and appear dark. The oval-shaped structures showing THG signals correspond to osteocytes which should fit into the dark holes shown in the SHG image. However, as can be seen in Figure 5.3(a), the mechanical drift of the sample during the time between imaging the corresponding frames in 2PM and 3PM stacks (~5 min) caused motion artifacts on the superposed image. The location for the same osteocyte shifted ~6 μm between the SHG and THG images. This motion artifact is minimized by the simultaneous acquisition of 2PM and 3PM stacks and the misalignment between SHG and THG images is significantly reduced as shown in Figure 5.3(b). Figure 5.2 Acquisition and verification of the multimodal MPM with simultaneous 2PM and 3PM imaging. (a) Focal shift between the two excitation wavelengths; (b) 2PM and 3PM signal spectra; (c) process for crosstalk removal and adjusting the depths between 2PM and 3PM stacks. The merged image is the superposition of single-channel images with the blue, red, and green marks, respectively. THG image is denoised before merging. Red – 2PEF; blue – SHG; green – THG. Scale bar is 40 μm. 90 Figure 5.3 Motion artifacts comparison between (a) sequential and (b) simultaneous acquisition of 2PM and 3PM images of a mouse femur. While the chromatic aberration of the miniaturized objective causes the scanning focal shift of ~80 μm and makes adjustment of stack images necessary when creating superposed images, the simultaneous scanning of two planes can potentially provide advantages in reducing thermal damage during multimodal imaging. To illustrate this, we have performed simulations of laser heating on tissue with and without the focal shift. The simulation is carried out using Ansys Workbench (Ansys Inc.) and the results are shown in Figure 5.4. A volume of 200 × 100 × 100 μm3 of water is used to simulate the biological tissue and the environmental temperature is set to be 22 °C. Figure 5.4(a) shows the configuration of the simulated focal volume of single-plane imaging with a heat power of 4 mW applied. Figure 5.4(b) shows the configuration of dual-plane imaging where the two focal points are separated by 80 μm and a heat power of 2 mW is applied to each focal volume. The simulated temperature distribution across the section of the simulated volume under the settings of single- and dual-plane imaging is shown in Figure 5.4(c) andFigure 5.4(d), respectively. With the same total heat power, the highest temperature for single- and dual- 91 plane imaging is ~57 °C and ~40 °C, respectively. Therefore, by imaging from two planes separated by 80 μm, the focal point temperature rise has been reduced by ~50%, which can dramatically reduce tissue damage. This is critical in MPM where a large amount of laser power is needed in order to excite 2PM and 3PM signals. Figure 5.4 Configuration of thermal analysis and obtained temperature distribution of single vs. dual focal-plane imaging under the same total heat power. (a) and (b) are for single focal-plane imaging; (c) and (d) are for dual focal-plane imaging. The total heat power is 4 mW and the environment temperature is set to be 22 °C. 5.4 Combined 2PM and 3PM imaging of biological samples Multimodal MPM with simultaneously acquired 2PM and 3PM images are demonstrated in Figure 5.5 on biological samples. The 2PM (2PEF and SHG) and 3PM (THG) signals are acquired simultaneously and label-free, enabling a comprehensive assessment of intricate microstructures while minimizing motion artifacts. Due to the focal shift, the THG image stack is shifted by 80 μm when merging the three channels. 92 Figure 5.5(a) shows multimodal MPM images of a fresh yew leaf. The SHG and THG channels show low-level signals and are denoised by KNS (iteration N = 1). Cell walls can be clearly seen as bright stripes in 2PEF on the leaf surface and dark stripes beneath the leaf surface. Sub-cellular structures can be identified as dark circular features on the images from a deeper depth (>20 μm). THG contrast shows chloroplasts inside leaf cells underneath the surface. Figure 5.5(b) shows multimodal MPM images of a healthy human trabecular bone tissue fixed by formalin and mounted into a wax mold. The THG images are denoised by KNS (iteration N = 1). Osteocytes and canaliculi (lacuno-canicular networks) are visualized mainly by THG but also 2PEF contrasts from near the surface up to ~100 μm deep into the tissue. Osteocytes play a vital role in bone homeostasis and mechano-sensation and are shown as oval-shaped structures in THG and 2PEF [142,143]. As THG requires interfaces [65,178], osteocytes may generate THG signals due to their complex morphology and refractive index variations created by the gap of 0.5–1.0 μm between the osteocyte cell body and the lacuna [144,145]. The 2PEF from osteocytes could be generated from endogenous fluorophores like NADH in the cytoplasm. The tiny fibrous structures in the image shown from depths 43, 55, and 74 μm are most likely canaliculi (canals where processes of osteocytes are embedded), which will maintain some extent of their micro-structures even with no metabolism. Given the sensitivity of THG to interfaces [65,178] and the role of canaliculi as essential communication channels for osteocytes, it is plausible that the observed THG signals originate from interfaces in the intricate networks of those cellular processes. The THG channel does not have sufficient SNR to image deeper beyond ~100 μm while the 2PEF channel can image up to the depth of ~170 μm. This is because three-photon nonlinear effects are generally weaker than two-photon nonlinear effects and the focal spot at a longer excitation wavelength (1580 nm, for 3PM) is not as tight as that at a shorter excitation wavelength 93 (790 nm, for 2PM). The 2PEF contrast also visualizes endogenous fluorophores in other biomolecules and structures such as cartilage within the bone matrix. The SHG contrast delineates the collagen bundles in different depths, showing relatively regular distribution and organization, which confirms the healthy status of the sample. Figure 5.5 Simultaneous 2PM and 3PM imaging of biological samples. Red – 2PEF; blue – SHG; green – THG. Scale bar is 40 μm. 5.5 Discussion and conclusions In this chapter, a compact multimodal MPM system has been developed to perform 2PM and 3PM imaging simultaneously. The dual-wavelength excitation enables the acquisition of multiple contrasts including 2PEF, SHG, and THG all label-free, which can obtain complementary information about tissues. To achieve simultaneous acquisition of 2PM and 3PM signals, several challenges are addressed. The 2PEF, SHG, and THG channels are separated spectrally by properly selected dichroic beam splitters. The challenge of partial wavelength overlapping between 2PEF 94 and THG has been discussed and a signal-subtraction method is proposed to remove the leaked signal from images. The benefit of reducing motion artifacts and mechanical drift by simultaneous acquisition of multiple channels has been demonstrated. While the 2PM and 3PM image stacks are acquired simultaneously in our study, there is a focal shift of ~80 μm between the 2PM and 3PM stacks. Thus, there still exists a time delay between the corresponding 2PM and 3PM frames from the same tissue depth. This time delay depends on the focal shift, depth scanning step size, and frame rate. To further reduce this time delay and its impact on motion artifacts during in-vivo imaging, several approaches can be carried out in future work. First, a faster scanning speed of up to ~4 fps can be applied to reduce the time delay by 10 times. Second, the focal shift can be engineered and reduced by an achromatic design of the objective. Moreover, the focal shift can also be controlled if separate beam paths for the 790 and 1580 nm wavelengths are implemented. Nevertheless, this will increase the complexity and dimension of the system and thus is not ideal for the compact system. On the other side, the focal shift between 790 and 1580 nm can potentially be utilized to acquire images from two planes simultaneously. In addition, since the two beams are focused at two different depths, it can bring about a higher endurance of the total laser power compared to when the two wavelengths are focused on the same plane. When the focal shift is reduced, the laser-induced temperature rise in tissue can increase. Based on our thermal simulation, a focal shift of ~20–40 μm could be a good balance. To demonstrate its capability, depth-resolved multimodal MPM images from biological samples are demonstrated with 2PEF, SHG, and THG signals acquired simultaneously by our compact system. The multimodal imaging approach offers a comprehensive view of microstructures in tissues, facilitating a deeper understanding of tissue composition and potential cellular interactions. Our technique of simultaneous acquisition of 2PM and 3PM signals shows 95 great potential in improving MPM imaging by accelerating the imaging process and reducing possible image artifacts caused by tissue motion and mechanical drift during sequential acquisition. 96 Chapter 6: Summary and future work 6.1 Summary MPM is a non-invasive high-resolution imaging technique that can reveal structures and chemicals in biological tissues label-free. It is an emerging tool that has promising potential in clinical medical imaging. However, traditional MPM systems are mostly limited to research labs with very few clinical applications. They are typically built on free-space lasers and microscope platforms which are bulky, expensive, and require precise alignments. In this dissertation, we made advancements in making the technology one step closer to clinical use. A compact multimodal MPM system is developed with the miniaturization achieved by the fiber laser source, compact objective design, SMA depth scanner, and MEMS planar scanner. Label-free multi-contrast imaging is demonstrated with simultaneous two- and three-photon imaging. The following specific contributions are made to address the challenges described in Section 1.8 of Chapter 1. First, to understand the origin of the contrast signals and the impact of phase matching in imaging tissues, we theoretically analyzed harmonics generation with a focused Gaussian beam. We extended R. W. Boyd’s theory of harmonic generation under a focused Gaussian beam and derived analytical solutions of the phase-matching factor integral for both SHG and THG. The results showed that while the efficiency of harmonic generation vanishes for infinite uniform mediums with normal dispersion, harmonics can be generated when there is an interface that breaks the uniformity. The interface dependence for SHG and THG was verified by simulations and experiments. An optimization process of the confocal parameter was demonstrated based on the properties of the sample. This theoretical study paves the way and provides explanations for tissue-specific sources of contrasts in multimodal MPM imaging. 97 Second, we designed and fabricated a miniaturized depth-scanning objective. As traditional MPM often relies on bulky microscope objectives and depth scanners, to bring the technology into clinical use, we developed the compact objective with built-in depth-scanning capability. The objective is based on a compact two-lens design which consists of an aspheric lens and a plano-convex lens. The aspheric lens is mounted in a miniaturized SMA actuator which enables depth scanning. The objective is calibrated and characterized for its performance of an NA of ~0.5, an actuation range of 370 μm, and a scanning error of <1 μm. The SMA-based solution of depth-scanning is more compact compared with traditional piezo-electric depth-scanning; and has advantages in scanning range and cost compared with ETL-based depth scanning. Our depth-scanning objective also provides more accurate and faster actuation compared with previous-reported SMA-based depth scanning. The compact objective contributes to the miniaturization of the MPM system and has great potential to be used in a clinical handheld imaging head in the future. Third, to improve the quality of MPM images and to visualize weak-signal channels in merged multimodal images, we developed a kernel-based denoising algorithm KNS. With kernel size chosen by tissue feature dimensions, a kernel is generated centered on each pixel on an image, and the brightness of the central pixel is scaled by a nonlinear equation with variables based on the global and local (kernel) averages. The denoising method was demonstrated on various MPM images acquired from animal and plant samples to verify its performance. Comparisons between KNS and commonly used traditional denoising methods as well as the self-supervised machine-learning method N2V were conducted, and the results showed that KNS outperforms those methods in denoising low-signal MPM images. In merged multimodal images consisting of channels with highly inconsistent SNRs, KNS improves the readability of low-signal channels. 98 KNS provides an effective denoising approach for low-signal images whenever ground truth images are not available. The low stress on computation power ensures video-rate fast denoising with can contribute to clinical real-time imaging. Finally, we demonstrated a compact depth-scanning multimodal MPM system capable of simultaneously acquiring two- and three-photon images all label-free. To enable label-free multimodal imaging in the compact system, the MPM system utilized two excitation wavelengths of ~790 and ~1580 nm from a single fiber source. The 2PEF, SHG, and THG channels are separated spectrally by properly selected dichroic beam splitters. To address the challenge of partial wavelength overlapping between 2PEF and THG, a signal-subtraction method is demonstrated to remove the leaked signal from images. Compared with sequential acquisition used by traditional two- and three-photon multimodal MPM, the simultaneous acquisition of multimodal signals reduces motion artifacts and mechanical drifts. Depth-resolved multimodal MPM images from biological samples are demonstrated with 2PEF, SHG, and THG signals acquired simultaneously. The multimodal imaging approach offers a comprehensive view of microstructures in tissues, facilitating a deeper understanding of tissue composition and potential cellular interactions. By achieving label-free volumetric images of biological tissues with comprehensive structural and biochemical details, the compact multimodal MPM with simultaneous two- and three-photon imaging and depth scanning shows great potential in clinical applications. There are several limitations of the depth-resolved multimodal MPM system. The two-lens objective has no achromatic design and that caused an axial (depth) focal shift of ~80 μm between the 2PM and 3PM stacks. As discussed in Chapter 5, this focal shift results in a time delay between the corresponding 2PM and 3PM frames from the same tissue depth. However, this focal shift also 99 brings about the advantages of a lower laser-induced temperature rise in tissue as well as a potential in dual-plane imaging. The focal shift can be optimized based on this trade-off by an achromatic engineering of the objective and the time delay can be minimized by implementing a higher scanning speed or separate beam paths for two- and three-photon excitations. Another current limitation is the low THG signal level. As a three-photon nonlinear effect, the efficiency of THG is significantly lower than two-photon nonlinear effects such as SHG and 2PEF and hence THG requires much higher photon density for sufficient signal excitation. Our current system utilizes a laser source with a pulse repetition rate of 50 MHz which is typical for two-photon imaging, however, for THG imaging, a laser source with a lower repetition rate (e.g., 1–2 MHz) can provide higher peak power and thus improve the signal level. The moderate NA (~0.5) of our objective can be another reason for the low THG signal, as it cannot form a focal point as tight as that formed by a high-NA objective. The objective has no achromatic design and the focusing quality of the objective also limits the resolution of images. 6.2 Future work In future work, several potential developments can be carried out to address current limitations and further improve the performance of the depth-resolved multimodal MPM system. To improve the focusing quality, signal strength, and image resolution, an objective with achromatic design, higher NA, and better fabrication processes can be developed. However, such an objective will contain more lenses and there is a trade-off between the compactness and performance of the objective. A high-NA chromatic and field-corrected GRIN lens (GT-MO-080-032-ACR-VISNIR series, Grintech) can be a potential choice for future development of miniaturized objectives. Compared to traditional GRIN lenses, the newly-developed GRIN lens 100 has a significantly increased usable FOV (field radius > 120 μm) [207]. One difficulty is to implement depth-scanning into GRIN-lens-based objectives. A potential solution is to actuate the focusing lens in front of the back aperture of the GRIN lens. However, in this case, the actuation range needed is typically 10–20 times larger than the depth scanning range, and hence an actuator with a much higher range (which could be bulky) is required. On the other hand, the laser source can be further optimized to improve signal intensity and penetration depth. Since the THG signal is inversely related to the square of the repetition rate [208], under the same average power, a laser with a lower repetition rate can be beneficial to THG signal intensity. However, the low repetition rate will set a limit on the imaging speed, and a repetition rate between 1–5 MHz can be optimal in future developments. Another issue of the current laser source is that the 3PM excitation wavelength of ~1580 nm falls in a relatively high absorption range of water. This affects the penetration depth and can cause heat damage to the tissue. To reduce water absorption, it will be helpful to shift the 3PM excitation wavelength towards 1700 nm. Meanwhile, the wavelength of the frequency doubling of 1700-nm is 850 nm which might be too long for efficient excitation of NADH in 2PEF imaging [209–212]. Thus, a wavelength between 1600 to 1700 nm (e.g. 1650 nm) could be a balanced choice for future development to reduce water absorption while keeping the capability of simultaneous label-free multimodal imaging. A soliton fiber laser can potentially be utilized to shift the wavelength towards 1700 nm and also compress the pulsewidth. Adaptive optics can also be implemented in future work to improve the imaging quality and penetration. A deformable mirror or a spatial light modulator can be utilized to compensate for the wavefront aberration of the excitation beam in order to achieve diffraction-limited focusing. When using GRIN objectives which typically have high spheric aberration, adaptive optics can 101 compensate for the aberration and provide a larger useful FOV [175]. With adaptive optics, both system aberrations caused by optical components and misalignment, as well as tissue-induced aberrations can be compensated to improve focal point quality and keep the beam tightly focused in deep tissue. However, adaptive optics require extra components which bring about challenges for a compact design. Especially in the direct wavefront sensing scheme where a Shack-Hartmann sensor (contains a camera and a lenslet array) is required in addition to wavefront correcting components [175,213,214]. Therefore, indirect wavefront sensing with minimal required hardware components such as the modal approach [215] and the DASH (dynamic adaptive scattering compensation holography) interferometric focus sensing [216,217] will be first considered in the implementation of adaptive optics for the compact MPM system. However, as adaptive optics also requires a beam expansion path that makes the system bulky, there is still a trade-off between the utilization of adaptive optics and compactness. Potential applications of the compact MPM system include skin imaging and oral cavity imaging. Due to the use of miniature optics and scanners, our compact system can be packaged into a handheld imaging head with a fiber connection between the imaging head and laser controller. Compared to the articulated arm design, the handheld imaging head design with fiber connection will bring about great flexibility for patient imaging. 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Thesis/Dissertation
eng
Electrical and Computer Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Tang, Shuo
Development of compact multimodal two-photon and three-photon microscopy
Text