Applied Science, Faculty of
Biomedical Engineering, School of
DSpace
UBCV
Siadati fini, Seyedeh Mahsa
Master of Applied Science - MASc
University of British Columbia
Optical coherence tomography (OCT) is a non-invasive optical imaging technique to capture in-vivo, three-dimensional (3D) image data with micrometer resolution. OCT has become a valuable tool for ophthalmologists in detecting and monitoring eye diseases like age-related macular degeneration (AMD), glaucoma, and diabetic retinopathy (DR) by providing valuable information for diagnosis and determining the best course of treatment. OCT angiography (OCTA) is a functional extension of OCT which allows for non-invasive visualization of blood flow in the retina and choroid. OCTA provides detailed information about microvasculature, which is not possible with traditional angiography techniques. However, OCT and OCTA images are susceptible to degradation from motion artifacts induced by the involuntary human subjects’ head and eye movement. Multi-volume acquisition and averaging is a software-based approach to correct the motion artifacts by registering several motion-corrupted images to a single reference image or multiple reference images in a post-processing step. Nonetheless, most software-based algorithms have been more specifically designed for a specific system with a limited field-of-view (FOV) range. In this study, we implemented a robust 3D algorithm for the precise registration of multiple volumes to the coordinates of one volume chosen as the reference volume. Tests were carried out on various datasets obtained from different types of custom-built and commercially available systems to determine the reliability of the proposed algorithm. A two-dimensional (2D) version of this method is then applied to investigate retinal perfusion heterogeneity, which may enhance our understanding of retinal diseases such as glaucoma.
Functional 3D retinal imaging using OCT technology and a novel, system-agnostic registration algorithm by Seyedeh Mahsa Siadati fini BASc., Sharif University of Technology, 2020 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Biomedical Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2023 © Seyedeh Mahsa Siadati fini, 2023 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Functional 3D retinal imaging using OCT technology and a novel, system-agnostic registration algorithm submitted by Seyedeh Mahsa Siadati fini in partial fulfilment of the requirements for the degree of Master of Applied Science in Biomedical Engineering Examining Committee: Myeong Jin Ju, Assistant professor, Biomedical Engineering, UBC Supervisor Shou Tang, Professor, Electrical and Computer Engineering, UBC Supervisory Committee Member Jane Z. Wang, Professor, Electrical and Computer Engineering, UBC Supervisory Committee Member iii Abstract Optical coherence tomography (OCT) is a non-invasive optical imaging technique to capture in-vivo, three-dimensional (3D) image data with micrometer resolution. OCT has become a valuable tool for ophthalmologists in detecting and monitoring eye diseases like age-related macular degeneration (AMD), glaucoma, and diabetic retinopathy (DR) by providing valuable information for diagnosis and determining the best course of treatment. OCT angiography (OCTA) is a functional extension of OCT which allows for non-invasive visualization of blood flow in the retina and choroid. OCTA provides detailed information about microvasculature, which is not possible with traditional angiography techniques. However, OCT and OCTA images are susceptible to degradation from motion artifacts induced by the involuntary human subjects’ head and eye movement. Multi-volume acquisition and averaging is a software-based approach to correct the motion artifacts by registering several motion-corrupted images to a single reference image or multiple reference images in a post-processing step. Nonetheless, most software-based algorithms have been more specifically designed for a specific system with a limited field-of-view (FOV) range. In this study, we implemented a robust 3D algorithm for the precise registration of multiple volumes to the coordinates of one volume chosen as the reference volume. Tests were carried out on various datasets obtained from different types of custom-built and commercially available systems to determine the reliability of the proposed algorithm. A two-dimensional (2D) version of this method is then applied to investigate retinal perfusion heterogeneity, which may enhance our understanding of retinal diseases such as glaucoma. iv Lay Summary To better understand the causes of blindness-causing retinal diseases and develop effective treatments, it is crucial to develop innovative imaging tools that can accurately and non-invasively detect and monitor the progression of these conditions. With an estimated 5.59 million Canadians currently living with eye diseases that could potentially lead to vision loss, such as age-related macular degeneration (AMD), diabetic retinopathy (DR), and glaucoma, the need for advanced imaging technologies has never been more pressing. By investing in these tools, we can improve early diagnosis, monitor disease progression, and ultimately improve the quality of life for those affected by these conditions. Optical coherence tomography (OCT) is a non-invasive three-dimensional (3D) imaging technique that allows the acquisition of in-vivo volumetric data of the retina and other ocular structures. Optical coherence tomography angiography (OCTA) is a functional extension of OCT that provides noninvasive visualization of blood flow in the retina and choroid. Despite their wide range of applications in ophthalmology, OCT and OCTA still face challenges in terms of image quality due to motion artifacts. One approach for addressing motion artifacts involves registering and averaging multiple volumes acquired at the same position. Several registration algorithms have been developed to this end, but most of them are generally designed to work only with a specific OCT system and acquisition protocol. The objective of this study is to develop a robust, system-agnostic registration algorithm that is independent of any system specifications. v Preface This thesis is the result of a collaborative effort between the author and other members of the team. It presents an original and previously unpublished work. In Chapter 1, Figure 1.1 has been obtained from a reputable source with the necessary permissions. Additionally, it is important to acknowledge that the data used in Chapter 4 was collected by Y. Miao at the Vancouver General Hospital Eye Care Centre (ECC), while the processing of the data was solely the responsibility of the author. The idea presented in Section 4.1.3 originated from the work of Kurokawa et al. (2021) and the code was implemented throughout the chapter by the author. Finally, the method presented in Chapter 5 was designed by A. Athwal and published in the master's thesis entitled "3D Image Registration and Retinal Perfusion Heterogeneity Analysis of Clinical Optical Coherence Tomography Angiography." vi Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ............................................................................................................................... iv Preface .............................................................................................................................................v Table of Contents ......................................................................................................................... vi List of Tables ................................................................................................................................ ix List of Figures .................................................................................................................................x List of Abbreviations ...................................................................................................................xv Acknowledgements ................................................................................................................... xvii Dedication ................................................................................................................................. xviii Chapter 1: Introduction ................................................................................................................1 1.1 Anatomy of the eye ......................................................................................................... 1 1.2 Ophthalmic imaging modalities ...................................................................................... 2 1.3 Outline of the Thesis ....................................................................................................... 6 Chapter 2: Optical coherence tomography .................................................................................8 2.1 Fundamental OCT theory ............................................................................................... 8 2.2 Advanced OCT technologies ........................................................................................ 12 2.2.1 Functional extension of OCT ................................................................................ 12 2.2.2 High-speed OCT ................................................................................................... 15 2.2.3 Cellular-resolution OCT ....................................................................................... 16 2.3 Summary ....................................................................................................................... 18 Chapter 3: Image registration ....................................................................................................19 vii 3.1 OCT and motion artifacts .............................................................................................. 19 3.2 OCT image registration algorithms .............................................................................. 21 3.3 Summary ....................................................................................................................... 24 Chapter 4: System-agnostic 3D registration of OCT volumes .................................................25 4.1 Motivation ..................................................................................................................... 25 4.2 OCT instrumentation and data acquisition ................................................................... 25 4.3 Methods......................................................................................................................... 26 4.3.1 Pre-processing ....................................................................................................... 26 4.3.2 Coarse registration ................................................................................................ 31 4.3.3 Affine registration ................................................................................................. 32 4.4 Results ........................................................................................................................... 33 4.4.1 Qualitative registration performance validation – DS-OCT system ..................... 33 4.4.2 Qualitative registration performance validation – PD-OCT system ..................... 38 4.4.3 Qualitative registration performance validation – SAO-OCT system .................. 40 4.4.4 Qualitative registration performance validation – commercialized OCT device . 43 4.4.5 Quantitative registration performance evaluation ................................................. 44 4.5 Discussion ..................................................................................................................... 49 4.6 Summary ....................................................................................................................... 51 Chapter 5: Clinical applications of image registration ............................................................52 5.1 Motivation ..................................................................................................................... 52 5.2 OCTA imaging protocol ............................................................................................... 53 5.3 Methods......................................................................................................................... 53 5.3.1 OCTA image registration and averaging .............................................................. 53 viii 5.3.2 Vessel segmentation using Deep Neural Network (DNN) ................................... 54 5.3.3 Coefficient of Variation (CoV) calculation .......................................................... 55 5.4 Results ........................................................................................................................... 56 5.4.1 Clinical outcome measure ..................................................................................... 56 5.5 Summary ....................................................................................................................... 59 Chapter 6: Potential Future work and Conclusion ..................................................................60 6.1 Future Direction ............................................................................................................ 60 6.1.1 High-speed system-agnostic 3D volume registration with sub-pixel precision .... 60 6.1.2 OCT-based 3D perfusion heterogeneity analysis ................................................. 60 6.2 Conclusion .................................................................................................................... 62 Bibliography .................................................................................................................................63 ix List of Tables Table 3.1 Summary of the typical registration methods ............................................................... 22 Table 4.1 Summary of systems specifications .............................................................................. 26 Table 5.1 Demographic of study participants ............................................................................... 53 Table 5.2 Comparison of average and standard deviation of CoV for different layers of healthy and glaucomatous eyes ................................................................................................................. 57 x List of Figures Figure 1.1 Cross section of a human eye anatomy [1]. ................................................................... 1 Figure 1.2 Fundus Photograph showing the optic nerve head and surrounding retina in a healthy eye. .................................................................................................................................................. 3 Figure 1.3 Fluorescein angiography photograph of the retina ........................................................ 4 Figure 1.4 Fundus autofluorescence photograph of the retina. ....................................................... 5 Figure 1.5 Optos ultra-widefield scanning laser ophthalmoscope (SLO) image of a patient with Retinitis pigmentosa (RP). .............................................................................................................. 6 Figure 2.1 Illustration of the fiber-based Michelson interferometer. When using a light source, the light is divided into two parts by a coupler. These parts travel through separate arms of the interferometer, known as the reference arm and the sample arm. As the light reflects back from the reference mirror and the sample, it reunites at the coupler, creating an interference pattern. This pattern is then captured by a singular point detector. ..................................................................... 8 Figure 2.2 (A) Illustrates the SD-OCT system where the backscattered light from the sample and reference arms are detected by an array of detectors. (B) Illustrates the schematic of SS-OCT system where a tunable laser source is used to select a narrow line width of the light source ..... 10 Figure 3.1 Illustration of (A) A-line, (B) Fast B-scan and (C) Volumetric OCT ......................... 19 Figure 3.2 Illustration of a 3D OCT, XY plane shows en-face projection along the depth (z), XZ plane illustrates fast B-scan or projection along the Y-direction, YZ plane shows the slow B-scan or projection along the X-direction. .............................................................................................. 20 Figure 4.1 Graphical illustration of multi-volume registration and averaging algorithm ............. 26 Figure 4.2 Illustration of how the axial matching algorithm aligns the axial position of the target volume with the reference volume. In this example M is set to be 10 and P is set to be 5. ......... 30 xi Figure 4.3 Illustration of auto-motion removal algorithm steps ................................................... 31 Figure 4.4 The steps of Affine registration: After Coarse registration, the volume is projected along the depth and rotated at various angles ranging from -α° to α° to identify the optimal rotation angle that yields the highest cross-correlation with the reference image. Subsequently, the identified optimal rotation angle is applied to all depth positions. The value of α used in our tested systems was set to 1. ................................................................................................................................... 33 Figure 4.5 Comparison of the single OCT volume with the registered averaged volume acquired from a custom-built dual-spectrometer SD-OCT system. (A, C) SVP of the reference volume and the averaged volume, respectively. The red square in (A) represents the miscorsaccadic motion artifact, while the corresponding green square in (C) represents the corrected motion artifact. (B, D) Deep vasculature of the reference volume and the averaged volume, respectively. Further, blue square indicates an improvement in vasculature contrast and connectivity of the yellow square.35 Figure 4.6 Illustration of the OCT/OCTA volumes acquired using the high-SNR mode of the custom-built dual spectrometer system. (A, B) Comparison between the single reference and 20 registered and averaged OCT volumes for the SVP. (C, D) Comparison between the single reference and averaged OCT volumes for the DVP. The red squares correspond to the same regions in the OCTA of the reference and averaged volumes, which have been magnified at the bottom for better visibility. (E, F) SVP of the corresponding single and averaged OCTA volumes. The red square in (E) corresponds to the motion artifact which is corrected in (F). Further, yellow squares show the improvement in contrast of the SVP. (G, H) DVP layer of the single and averaged OCTA volumes. Green square corresponds to the microsaccadic motion artifact in the single volume which is corrected after registering and averaging multiple volumes. ......................................... 37 xii Figure 4.7 Comparison of reference and registered averaged volumes acquired from PD-OCT system. (A) NFL of the single volume. Three regions are enclosed in the colored squares in the figure and magnified below the image. The red arrow shows the motion artifact. (B) NFL of the averaged volume corresponds to the NFL of the reference volume. The same regions are marked on the figure and magnified below the image. The red arrow points to the corresponding motion artifact location in the reference volume which is corrected in the averaged volume. (C) Representative B-scan of the single volume. (D) Representative B-scan of the averaged volume corresponds to the B-scan in (C). (E) Representative B-scan of the single volume overlayed with the DOPU signal. (F) Representative B-scan of the averaged volume overlayed with the DOPU signal. ............................................................................................................................................ 39 Figure 4.8 Performance of the 3D registration algorithm on the OCT volumes acquired using the custom-built SAO-OCT system. (A, B) Illustration of cone mosaic layer for the reference and registered averaged volume. The irregularity of the photoreceptors has been corrected after the 3D registration. (C, D) Projection of the RPE layer for the reference and the averaged volumes respectively. (E, F) Projection of the DVP for both reference volume and the averaged volume which indicates the improvement of the contrast and therefore connectivity of the vasculatures, and reduction in the speckle noise, after registration and averaging. ........................................... 41 Figure 4.9 Illustration of the performance of affine registration in improving the registration and averaging result. (A) Shows the overlay of the reference volume and the coarse-registered volume. (B) shows the overlay of the reference volume and the fully registered volume (coarse and affine registered). Green and magenta show the reference volume and the averaged volume, respectively. The white pixels correspond to the matching pixels of two volumes. It is evident from the image xiii that the number of matching pixels has increased after the affine registration, which indicates that the two volumes align more effectively after full registration. ..................................................... 42 Figure 4.10 Illustration of the OCT/OCTA volumes acquired using commercialized machine (Zeiss Plex Elite 9000). (A, B) Comparison between the single reference and 10 registered and averaged OCT volumes for the SVP. (C, D) Comparison between the single reference and averaged OCT volumes for the DVP. (E, F) SVP of the corresponding single and averaged OCTA volumes. (G, H) DVP of the single and averaged OCTA volumes. The regions of the images enclosed in the colored squared are magnified at the bottom of the image for better visualization and comparison. ............................................................................................................................ 44 Figure 4.11 Numerical evaluation of the registration performance using MSE and MS-SSIM metrics with the increase of averaging numbers. (A) Shows MSE and MS-SSIM evaluation of the OCT volumes acquired from the dual spectrometer system using high speed mode. (B) Illustrates MSE and MS-SSIM evaluation of the OCT volumes acquired from the high SNR mode of the dual spectrometer system. (C) Shows MSE and MS-SSIM evaluation of the OCT volumes acquired from SAO-OCT system. (D) Illustrates MSE and MS-SSIM evaluation of the OCT volumes acquired from the custom-built wide-FOV PD-OCT system. (E) Shows MSE and MS-SSIM evaluation metrics of the OCT volumes acquired from commercialized OCT system (Zeiss Plex Elite 9000). .................................................................................................................................... 48 Figure 5.1 Pipeline of registration, averaging, segmentation and CoV processing for 10 OCTA images acquired from a Glaucomatous eye. (A) SVP layer extracted from a 3 × 3 mm2 reference image, (B) SVP of the averaged registered volume, (C) DNN segmented SVP, (D) CoV color map of the SVP. The color map shows from no variation (blue) to high variation (red), (E) DVP layer extracted from a 3 × 3 mm2 reference image, (F) DVP of the averaged registered volume, (G) DNN xiv segmented DVP, (H) CoV color map of the DVP. The color map shows from no variation (blue) to high variation (red). .................................................................................................................. 56 Figure 5.2 Representative comparison between two Glacouma and control eyes for SVP and DVP layers. (A-C) Illustrate the registered avaeraged image, binary mask and CoV map of the SVP of a glaucoamctouas eye, respectively. (D-F) Represent the registered averaged image, binarized image and the CoV map of the DVP the same glaucomatous eye, respectively. (G-I) Show the registered avaeraged image, binary mask and CoV map of the SVP of a healthy eye, respectively. (J-L) Illuustrate the registered averaged image, binarized image and the CoV map of the DVP the same control eye, respectively ...................................................................................................... 58 Figure 6.1 Processing pipeline results of image registration, averaging and CoV calculations on OCTA images. .............................................................................................................................. 61 Figure 6.2 Processing pipeline results of image registration, averaging and CoV calculations on OCT images. ................................................................................................................................. 62 xv List of Abbreviations AO-OCT: Adaptive optics optical coherence tomography AMD: Age-related Macular degeneration BRISK: Binary robust invariant scalable keypoints CoV: Coefficient of variance DVP: Deep vascular plexus DOPU: Degree of polarization uniformity DR: Diabetic retinopathy DS-OCT: Dual spectrometer optical coherence tomography DBD: Dual balanced detection ECC: Eye Care Centre FAZ: Foveal vascular zone FA: Fluorescein angiography FAF: Fundus autofluorescence FDA: Food and drug administration FFT: Fast Fourier transform FOV: Field-of-view FWHM: Full width half maximum GPU: Graphics processing unit IR: Infrared MSE: Mean squared error MS-SSIM: Multiscale structural similarity metric NCC: Normalized cross-correlation xvi OCT: Optical coherence tomography OCTA: Optical coherence tomography angiography OMAG: Optical Microangiography PD-OCT: Polarization diversity optical coherence tomography PS-OCT: Polarization sensitive optical coherence tomography POC: Phase-only correlation RP: Retinitis pigmentosa RPE: Retinal pigment epithelium SLO: Scanning laser ophthalmoscopy SIFT: Scale invariant feature transform SNR: Signal-to-noise ratio SD-OCT: Spectral domain optical coherence tomography SURF: Speeded up robust features SVP: Superficial vascular plexus SS-OCT: Swept source optical coherence tomography TD-OCT: Time domain optical coherence tomography VGH: Vancouver General Hospital 3D: Three-dimensional 2D: Two-dimensional xvii Acknowledgements I would like to extend my heartfelt gratitude and appreciation to various individuals who have played a significant role in my thesis journey. Firstly, I would like to express my sincere thanks to my supervisor, Dr. Myeong Jin Ju, who provided unwavering support, motivation, and guidance throughout my two-year graduation studies in the Computational Ophthalmic Imaging Lab (COIL). I would also like to acknowledge and thank my supervisory committee members, Dr. Shuo Tang and Dr. Jane Z. Wang, for their invaluable knowledge and expertise that was instrumental in my successful completion of this project. Additionally, I would like to express my appreciation to Dr. Zaid Mammo for his generous support which made my research possible. I am also grateful to Mr. Arman Athwal for his support and assistance throughout my journey, and all members of the COIL, including Dr. Yusi Miao, Jun Song, Mohammad Shahidul Islam, Yujie Hu, and Tiffany Tse, for their academic and moral support and valuable feedback on my work. I would also like to extend my thanks to Ms. Laura Vargas for her expert assistance and support during the initial stages of my study. Finally, I would like to express my deep gratitude to my family, whose unwavering belief in me kept me motivated and inspired throughout this process. Their support was invaluable, and I cannot thank them enough. xviii Dedication To my parents and my brother 1 Chapter 1: Introduction 1.1 Anatomy of the eye Human eyes are among the most intricate parts of the body and are the primary sense organ for visual perception. We receive most of our visual information about our surroundings through our eyes, which convert incident light into electrical signals that our brain can understand. Figure 1.1 illustrates the cross-section of the human eye. Figure 1.1 Cross section of a human eye anatomy [1]. Incident light passes through the cornea, which is a transparent dome-shaped surface at the front of the eye with no blood vessels to nourish [2]. This light is bent by the cornea in order to pass through the pupil. Light is then focused onto the retina by the intraocular lens, where it causes chemical reactions in the photoreceptor cells. Retina is a light-sensitive area at the back of the eye consisting of millions of cells tightly packed together. The retina is nourished by a network of microvasculature, which is composed of several retinal capillary layers. These layers compose vascular plexuses, including the superficial vascular plexus (SVP) and the deep vascular plexus (DVP). 2 1.2 Ophthalmic imaging modalities Significant advancements in ophthalmic imaging have transformed its role from a mere tool for documenting eye conditions to a sophisticated and powerful investigative tool that provides clinicians with unprecedented access to intricate eye structures and details that traditional clinical examinations cannot reveal.Through the use of imaging techniques, we have rapidly gained a more comprehensive understanding of the eye in health and disease, uncovering previously undetected conditions, providing a more comprehensive description of the disease phenotype, and allowing for a methodical assessment of treatment effectiveness and safety [3]. This section provides a brief overview of the conventional ophthalmic imaging modalities commonly used in clinical practice, including fundus photography, fluorescein angiography, fundus autofluorescence imaging, and scanning laser ophthalmoscopy. Optical Coherence Tomography (OCT) is another widely used imaging modality that utilizes light waves to create detailed cross-sectional images of the retina, which will be discussed in detail in Chapter 2. Fundus photography: Fundus photography is a technique that utilizes the fundus camera to capture high-quality images of various structures in the eye, including the retina, optic nerve head, macula, retinal blood vessels, choroid, and vitreous. Fundus photography images have a significant impact on the detection and screening of various causes of preventable blindness. These include diabetic retinopathy, age-related macular degeneration, and glaucoma. Over time, fundus images have improved considerably, making it easier to capture accurate and detailed images [4]. However, a significant limitation of this technique is that it produces a 2D representation of the 3D retinal tissues projected onto the imaging plane. This results in a loss of depth information and makes it challenging to accurately assess the thickness and topography of retinal structures. Moreover, fundus photography can produce artifacts and distortions due to variations in the 3 refractive index of the media and imperfections in the imaging system. Despite these limitations, fundus photography remains a valuable tool for diagnosing and monitoring various retinal conditions. Figure 1.2 represents a high-resolution, color image of the retina and optic nerve head captured using a fundus camera. The blood vessels of the retina appear as thin lines that branch out from the optic nerve head and extend toward the periphery of the retina. The optic nerve head, located in the center of the image, appears as a small circular or oval-shaped structure that connects the retina to the optic nerve. Figure 1.2 Fundus Photograph showing the optic nerve head and surrounding retina in a healthy eye. Fluorescein angiography: In 1930, Kikai [5] published a report on the intravenous injection of fluorescein and its observation in the retinal vessels. This led to the adoption of fluorescein angiography (FA) by pioneers in medical retina research, who used it to study retinal vasculature in health and disease [6]. FA is considered the gold standard for evaluating retinal circulation, as it allows for the assessment of the architecture of the blood vessels, blood flow, and leakage from damaged or diseased retinal vessels. Nevertheless, the technique has proven to be valuable in diagnosing and managing retinal diseases such as diabetic retinopathy, and age-related 4 macular degeneration. Figure 1.3 provides a representation of an FA image, serving as an illustrative example of its appearance and aiding in the understanding of this imaging tool's nature. Figure 1.3 Fluorescein angiography photograph of the retina Fundus autofluorescence imaging: Fundus autofluorescence (FAF) imaging is a method of capturing live images that can create a map of the location and distribution of natural or pathological fluorophores in the ocular fundus [7]. This imaging technique uses the same physical principle as fluorescein angiography, in which light excitation of certain molecules causes them to emit light at a longer wavelength. However, FAF imaging differs from fluorescence angiography in that it visualizes endogenous fluorophores, whereas the angiography detects exogenous molecules that are injected intravenously. FAF imaging does not require an injected agent, as the intrinsic molecules are already present. The FAF signal intensity is weaker than fluorescein angiography, which requires sensitive imaging systems [8]. FAF imaging can provide valuable information about the pathophysiology of diseases, aid in diagnosis, detect prognostic markers for disease progression, and assess the efficacy of treatment strategies in chorio-retinal diseases [7]. 5 Figure 1.4 depicts a FAF image, which serves as a visual example to illustrate the appearance and functionality of this imaging technique. Figure 1.4 Fundus autofluorescence photograph of the retina. Scanning laser ophthalmoscopy: The scanning laser ophthalmoscopy (SLO), which was first described in 1981, creates images of the retina and optic nerve head using monochromatic, low-power laser light and a confocal raster scanning approach. It has been shown that the images captured using SLO systems are comparable to those captured using monochromatic fundus shots, but they have better image quality, especially in patients with cataracts. Several wavelengths of laser enable the acquisition of FA images, near-infrared (IR) reflectance images and FA imaging [3]. Figure 1.5 illustrates an SLO image captured from a patient with retinitis pigmentosa (RP) which provides a detailed and comprehensive view of the retina. The wide field-of-view (FOV) provided by SLO imaging allows for a complete assessment of the retina in a single image, providing valuable information for the diagnosis and management of the patient's condition. The 6 non-invasive nature of SLO imaging, combined with its high resolution and ability to provide dynamic images, makes it an essential tool in the assessment of RP and other retinal disorders. Figure 1.5 Optos ultra-widefield scanning laser ophthalmoscope (SLO) image of a patient with Retinitis pigmentosa (RP). 1.3 Outline of the Thesis The remainder of the thesis is structured as follows. Chapter 2 provides an overview of OCT and its various functional and technical extensions, including OCT angiography (OCTA), polarization diversity OCT (PD-OCT), dual spectrometer OCT (DS-OCT), and adaptive optics OCT (AO-OCT). The chapter offers a comprehensive explanation of these extensions, including their background information, theory, and applications. Chapter 3 delves into the theory and applications of image registration. The chapter provides a detailed explanation of the importance of image registration and its various algorithms and methods, as well as their application in medical imaging. Chapter 4 introduces an innovative technique for motion artifact correction using 3D OCT image registration. The technique is described in detail, including its method and results, and 7 how it can enhance the clarity of vascular features in normal and diseased eyes. Chapter 5 explores the potential applications of the previously introduced registration methods and the registration method introduced in Chapter 4, to identify the temporal and spatial heterogeneity of the retina. The chapter provides a comprehensive analysis of the results and conclusions of these applications. Finally, Chapter 6 concludes the thesis with a summary of the findings and a discussion of potential future research. 8 Chapter 2: Optical coherence tomography 2.1 Fundamental OCT theory Optical coherence tomography (OCT) is a non-invasive, 3D, in-vivo optical imaging technique that enables cross-sectional imaging of a tissue sample [9, 10]. OCT instrument is analogous to ultrasound imaging; however, the imaging system uses near IR light instead of the sound wave. While the depth of penetration in tissue is only 1 – 2 mm, the depth resolution in OCT is on a micrometer scale, providing incredible detail of tissue structures in the axial plane. OCT imaging is based on the principle of light interference. The fundamental component of any OCT system is an interferometer setup, which is responsible for generating the interference signal that is used to reconstruct the image. Although there are several types of interferometer configurations that can be used in OCT, the optical fiber-based Michelson interferometer setup as shown in Figure 2.1 is commonly employed due to its simplicity. Figure 2.1 Illustration of the fiber-based Michelson interferometer. When using a light source, the light is divided into two parts by a coupler. These parts travel through separate arms of the interferometer, known as the reference arm and the sample arm. As the light reflects back from the reference mirror and the sample, it reunites at the coupler, creating an interference pattern. This pattern is then captured by a singular point detector. 9 In the time domain OCT (TD-OCT) system, light from a low-coherence light source is directed a fiber coupler and one of the beams is incident onto the sample to be imaged, while the second beam travels a reference path with a variable path length and time delay. The backscattered light from the sample is combined with reflected light from the reference arm, and then detected by a photodetector located at the interferometer's output. When the light source is coherent, the interference fringes are visible with varying relative path lengths. In the case of a low-coherence light source, the interference between the light reflected from the sample and reference path occurs only when their path lengths match within the coherence length of the light. The photodiode detects intensity modulations, which are also known as interference fringe bursts. The envelope of this signal directly provides information about the amount of back-reflection or back-scattering from the sample. In the system, the reference mirror is scanned in the depth (z) direction for each sample point, and the photodetector records the corresponding light intensity. This process generates a complete depth profile of the sample reflectivity at the beam position, which is referred to as an A-scan (amplitude scan), similar to ultrasound imaging. To produce a cross-sectional image, also known as a B-scan, the sample beam is moved laterally across the sample. Fourier domain OCT (FD-OCT), also known as frequency domain OCT, is the second generation of OCT technology that offers a more efficient implementation of low-coherence interferometry. Unlike TD-OCT, FD-OCT utilizes spectral information to create A-scans without requiring mechanical scanning of the optical path length. There are two established methods to obtain the spectral information of the interferometric signal - spectral domain OCT (SD-OCT) [11, 12] and swept-source OCT (SS-OCT) [13, 14]. In both methods, an interference spectrum, also known as spectral interferogram, is recorded and used to calculate the A-scan through Fourier 10 transformation. As shown in Figure 2.2 (A), the primary optical setup of SD-OCT is similar to TD-OCT, but the photodetector is replaced with a spectrometer. The spectrometer utilizes a diffractive element to spatially separate the different wavelengths into a line image, which is captured by a high-speed line scan camera. Each camera readout results in a spectral interferogram with a superposition of fringe patterns, as explained below. A superluminescent diode (SLD) is often used as a broadband light source due to its large bandwidth and relatively high-power output. As shown in Figure 2.2 (B), SS-OCT uses an optical source that rapidly sweeps a narrow linewidth over a broad range of wavelengths instead of a broadband light source. During one sweep, a high-speed photodetector sequentially detects each wavelength component of the interferometric signal, which relies on high-speed analog-to-digital (A/D) converters. Figure 2.2 (A) Illustrates the SD-OCT system where the backscattered light from the sample and reference arms are detected by an array of detectors. (B) Illustrates the schematic of SS-OCT system where a tunable laser source is used to select a narrow line width of the light source In OCT, the axial and transverse resolutions are decoupled from each other. The coherence length of the light source determines the axial resolution, allowing for high resolution regardless of beam focusing conditions. The detected interference signal is the electric-field autocorrelation of the light source, and its spatial width is the coherence length. Additionally, the envelope of the field autocorrelation is equal to the power spectrum's Fourier transform. Consequently, the axial 11 resolution, or the width of the autocorrelation function, is inversely proportional to the power spectrum's width. For a Gaussian spectral distribution source, the axial resolution, Δz, is determined as follows: ∆𝑧 = $2 𝑙𝑛 2𝜋 * +𝜆!"∆𝜆-, (2.1) where Δz and Δλ are the full widths at half maximum of the autocorrelation function and power spectrum, respectively, with λ0 representing the center wavelength of the source. The transverse resolution, which is the ability to distinguish between objects located perpendicular to the optical axis, is determined by the minimum spot size of the focused probing beam. This parameter is inversely proportional to the numerical aperture (NA) of the lens used for focusing. In other words, a higher NA lens will produce a smaller spot size and therefore provide better transverse resolution. The transverse resolution, Δx, is calculated as Equation 2.2: ∆𝑥 = $4𝜆𝜋 * $𝑓𝑑* = 1.27 $ 𝜆!𝑁𝐴*, (2.2) where 𝑑 represents the spot size of the probing beam, when projected onto the objective lens and 𝑓 represents the focal length of the objective lens. This objective lens, or set of lenses, is located at the end of the sample arm. In OCT systems, the center wavelength (λ0) is selected to achieve the highest possible depth of penetration into the examined tissue. In ophthalmic systems, wavelengths of approximately 840 nm or 1060 nm are commonly used to enable light penetration through the retinal pigment epithelium (RPE) and facilitate imaging of the choroid. It is also crucial to consider the ocular media's absorption, which can cause light attenuation, ultimately reducing the signal detected by the detector when it returns from the retina. The axial imaging depth refers to the range of axial 12 coverage in a single B-scan. This range is determined by the highest detectable fringe frequency, which decodes the maximum depth of the interference spectrum. Therefore, the imaging depth (zmax) is defined by the total number of sample points (N) across the full spectral width (Δλ) recorded, as follows: 𝑧#$% = 14 𝜆!"∆𝜆𝑁 (2.3) 2.2 Advanced OCT technologies OCT is a cutting-edge medical imaging technique [9]. This revolutionary technique has transformed the way ophthalmologists diagnose and treat eye-related conditions, providing them with a new level of insight into the structures and functions of the eye. Over the years, OCT has been constantly improved and modified to meet the evolving needs of the medical community. Efforts have been made to increase its functional capabilities, speed of data acquisition, and cellular resolution, all of which have led to a more effective and efficient diagnostic tool. As a result of these advancements, OCT has become an indispensable diagnostic tool for eye specialists, allowing them to view the eye with unprecedented detail and accuracy. It has greatly improved the accuracy and speed of eye-related diagnoses and treatments and has become an essential tool for eye care professionals to provide their patients with the best possible care. 2.2.1 Functional extension of OCT The importance of posterior-eye imaging in ophthalmology cannot be overstated, as numerous severe eye conditions, such as glaucoma [15-17], age-related macular degeneration [18, 19], and diabetic retinopathy [20, 21], have been linked to morphological deformation and malfunction of the eye. Ophthalmic OCT has emerged as a powerful diagnostic tool for the detection of such structural changes, due to its ability to perform in-vivo 3D imaging. However, 13 despite the widespread use of ophthalmic OCT, there is still much room for improvement in its imaging capabilities. One of the greatest challenges faced by the ophthalmology community is the need to find additional contrast mechanisms that can provide deeper physiological information beyond mere morphological structure. This will play a critical role in further expanding the clinical applications of OCT in the field of eye care. OCT-based Angiography (OCTA) technique is a functional extension of traditional OCT in ophthalmology. It has enabled in-vivo visualization of both tissue structure and blood flow through retinal vessels and choroidal capillaries, without the use of a contrast agent. With the aid of retinal layer segmentation and postprocessing, OCTA can extract and display angiograms at various depths. Clinical studies have shown that OCTA can detect and correlate with vascular defects in various retinal diseases, including diabetic retinopathy, retinal vein occlusion, retinitis pigmentosa, and age-related macular degeneration [22]. The basic principle of OCTA involves detecting variations in amplitude, intensity, or phase variance between sequential B-scans taken at the same location of the retina. The process begins by collecting a series of B-scans at the same transverse location (also known as BM-scan) and then aligning them. The degree of decorrelation in signal is then calculated, allowing visualization of only the moving part, which is assumed to be caused by cells moving within the bloodstream and thus indicative of blood flow. This procedure is repeated for different positions in the retina to create a 3D dataset, which is used to reconstruct en-face angiograms. Various methods have been developed to extract the flow signal from the OCT signal. These methods include speckle variance OCT, which illustrates the alterations over time in the backscattering coefficients of the sample, as well as optical microangiography (OMAG) method. Equations 2.4 and 2.5 explicitly define the speckle variance and OMAG methods. 14 𝑆𝑉&' = 1𝑁 :(𝐼(&' − 1𝑁:𝐼(&')(*+ )" ,)(*+ (2.4) 𝑂𝑀𝐴𝐺&' = 1𝑁 :|𝐼(-+ − 𝐼(|,).+(*+ (2.5) where i, j, and k indicate the frame, width, and axial position of the B-scan respectively, whereas 𝐼 represents intensity at the index and N represents the number of repeated acquisitions per BM-scan. Polarization-sensitive OCT (PS-OCT) is another functional extension of OCT that uses the polarization properties of light to extract additional information about the sample being imaged [23]. To obtain the polarization characteristics of a sample, such as a birefringence [24-26], optic axis [26], and degree of polarization uniformity [27], more advanced hardware and software are necessary as compared to those required for standard OCT [23, 28]. In order to accurately control the polarization state of light for a circularly polarized input algorithm, bulk optics [26] or polarization-maintaining fiber components must be employed [29]. Conversely, for a single-mode fiber-based system, multiple input polarization states are utilized through the utilization of polarization multiplexing schemes, such as electro-optic polarization modulation, path-length multiplexing, or frequency multiplexing, with the aid of Jones-matrix [30-32] and Stokes-vector-based algorithms [33, 34]. Additionally, the extraction of various polarization properties from tissues demands sophisticated processing algorithms to complement the increased hardware requirements. 15 As a simplified version of PS-OCT, polarization diversity OCT (PD-OCT) has been developed with minimal hardware extension such as polarization diversity detection to the standard OCT. PD-OCT can detect two orthogonal polarization interference signals, simultaneously. From the two different polarization OCT signals, the randomness of the polarization (RP) parameter can be measured. Among the various contrasts of polarization properties studied in ophthalmology, the degree of polarization uniformity (DOPU) is widely studied for RPE imaging based on the polarization scrambling property of melanin granules in RPE and choroid [35]. 2.2.2 High-speed OCT Since the first clinical use of OCT imaging devices in ophthalmology, there has been a constant drive to increase acquisition speed and minimize the acquisition time required for recording a complete and dense 3D volume stack of the retina, due to the presence of eye movements that can cause artifacts even in fixating eyes. Advances in OCT hardware design and components have led to a dramatic increase in imaging speed, with A-scan rates increasing up to a few megahertz (MHz), sparking a continuous pursuit to increase acquisition speed. Over the past 25 years, different approaches have been tried to improve the imaging speed of FD-OCT systems, including the use of faster swept sources for SS-OCT and multiple spectrometers for SD-OCT. In SS-OCT system, the acquisition speed is highly dependent on the sweeping rate of the laser source, and advancements in swept source technology have significantly improved the A-scan rate of SS-OCT systems [36-38]. However, it is important to note that high-speed scanning OCT can come with a downside. The reduced illumination time in high-speed scanning OCT can result in decreased sensitivity. This is because the amount of light delivered to the tissue decreases with increasing speed, which can lead to a reduction in SNR. This reduction in sensitivity can be 16 mitigated by increasing the light power or averaging multiple OCT data, but these approaches can introduce other potential drawbacks such as phototoxicity and motion artifacts. As such, finding the right balance between speed and sensitivity is crucial in optimizing OCT imaging for clinical applications. In SD-OCT systems, the line-scan camera in the spectrometer is the principal factor limiting imaging speed since the A-scan rate of the sensors in the camera constrains the maximum performance of the system. Although scanning speeds of SS-OCT depend on laser sweep rates, which have been greatly improved over the last decade, most line-scan cameras used in SD-OCT operate at maximum speeds of 250 kHz [39] with only the state-of-the-art line-scan camera with a full well capacity reaching up to 600 kHz [40]. Nonetheless, various methods exist to overcome the speed limitations of SD-OCT. One practical solution is the implementation of multiple spectrometers for detection. multi spectrometer configurations have been reported to achieve 1 MHz A-scan acquisition rates at 850 nm by using four detection spectrometers and a 1 × 4 optical switch assembly [41]. In addition to the increased acquisition speed, several studies have shown clear advantages in SNR and common noise suppression by applying a dual-balanced detection (DBD) technique with two spectrometers [42-45]. Recently, our group demonstrated dual-mode OCT imaging with the dual spectrometer OCT (DS-OCT) system; high-speed mode for 500 kHz A-scan rate with a simple electrical switch and high SNR mode for DBD as detecting two OCT signals with π phase shift [46]. 2.2.3 Cellular-resolution OCT When it comes to retinal imaging, the highest achievable transverse resolution is often limited by the optics of the eye itself, as it serves as the objective for any retinal-imaging instrument, including OCT. Two factors are primarily responsible for this limitation: the finite size 17 of the pupil of the eye, which introduces diffraction to the imaging light, and imperfections in the optical properties of the eye [47]. These imperfections introduce aberrations to the wavefront of the imaging light. These aberrations can degrade image quality in terms of sharpness and SNR. This degradation is more pronounced in instruments that use the entire pupil of the eye and are close to the maximum dilated eye pupil [48, 49]. To minimize the influence of aberrations, smaller imaging beam diameters (below ~2 mm) are typically used, although this comes at the cost of a relatively low transverse resolution. To enhance the transverse resolution of retinal optical instruments, it is better to use the fully dilated pupil of the eye for imaging. This ensures the best possible diffraction-limited resolution. However, the optics of the eye being imaged introduce aberrations into the wavefront, which prevent diffraction-limited performance. Therefore, these aberrations must be compensated or corrected, which can be achieved through the use of adaptive optics (AO). AO technology was originally designed to improve image quality in ground-based telescopes in astronomy but can now be used in retinal imaging to correct for the aberrations of the imaging wavefront [50]. Adaptive optics OCT (AO-OCT) is a high-resolution ophthalmic imaging technique that provides a comprehensive 3D image of retinal structures [41]. AO-OCT systems can be implemented using different approaches. One approach is sensor-based AO-OCT, which involves wavefront measurement and correction. Wavefront sensing is achieved using part of the imaging light or a separate light beacon, such as the Shack Hartman wavefront sensor (SHWS), which produces spots on the camera that correspond to one of the illuminated lenslets. Wavefront correction involves shaping the wavefront of the imaging beam using a correcting device such as a deformable mirror (DM), liquid spatial light modulators, or segmented mirrors. AO-OCT systems require high correction bandwidth (10 Hz or more) to 18 account for time-varying aberrations introduced by the eye. The root mean square (RMS) error of the residual wavefront is used to estimate the correction efficiency of an AO system, with an RMS value below λ/14 considered diffraction limited. Another approach is image-based AO-OCT, where aberration estimation and correction are done on OCT image data. Aberrations can be evaluated and corrected using image sharpness metrics. Image-based correction can be done in acquisition or post-processing. If the image-based correction is done in image acquisition, it is called sensorless AO-OCT (SAO-OCT), while if it is done in post-processing, it is called computational (or digital) AO. SAO-OCT systems use OCT images to assess wavefront changes, but aberrations are compensated serially, making the systems slow. New algorithms are being developed to improve temporal performance, and the use of low-cost correcting devices may make SAO-OCT systems commercially feasible. However, SAO requires a longer correction time and higher total light exposure and may benefit from a separate light beacon for the sensor in SHWS systems. 2.3 Summary This chapter explores the fundamental theory of OCT and how the volumes are structured, as well as advanced OCT technologies. To understand the image processing techniques employed in the remainder of this dissertation, it is necessary to have a thorough understanding of the appearance of normal OCT data in 2D and 3D, and different OCT technologies such as OCTA, PS-OCT, high-speed SS-OCT, DS-OCT, and AO-OCT. 19 Chapter 3: Image registration 3.1 OCT and motion artifacts OCT is a volumetric imaging technique that employs specific terminology to describe the volume images obtained. A-lines (depth scans) are used to measure the intensity profile of light scattering when a beam of light scans axially towards the tissue in the z-direction, as illustrated in Figure 3.1 (A). The fast B-scan, shown in Figure 3.1 (B), is a 2D image produced by moving the beam in the X-direction. Finally, a volumetric image is constructed by stacking fast B-scans in the Y-direction, as demonstrated in Figure 3.1 (C). Figure 3.1 Illustration of (A) A-line, (B) Fast B-scan and (C) Volumetric OCT It is possible to project a volume on the XY-plane by averaging all pixels in the Z-direction. En-face projection is achieved by averaging all pixels in the Z-direction which reveals vascular and cellular information of the layer of interest (Figure 3.2). 20 Figure 3.2 Illustration of a 3D OCT, XY plane shows en-face projection along the depth (z), XZ plane illustrates fast B-scan or projection along the Y-direction, YZ plane shows the slow B-scan or projection along the X-direction. A typical OCT scan takes a few seconds to complete. The data obtained from this OCT scan can be negatively impacted if there is any eye movement during the imaging process. One way to reduce eye movement during the imaging process is by instructing the subject to focus on a target. However, this approach may not be fully effective, as the eyes continue to experience fixational eye movements even during conscious fixation. Fixational eye movements consist of three distinct types of motion, including ocular tremor, ocular drift, and microsaccades [51]. Microsaccades which are the most severe motion artifacts, can adversely affect the image quality. Furthermore, patients with impaired focal vision may struggle to fixate, resulting in motion artifacts occurring more frequently and with a greater amplitude than healthy individuals [52, 53]. The existing solutions to the motion artifact problem can be categorized as either hardware-based 21 or software-based [54, 55]. Hardware-based solutions for correcting motion artifacts in OCT volumes can be divided into two categories – employing motion-tracking systems mounted on the OCT machine to correct the OCT volumes by moving B-scans in real-time [56-59], and using high-speed imaging systems to acquire images in a short time interval before motion artifacts are captured [60-62]. However, the high-speed imaging systems drastically decrease the SNR of the image [63], and the additional motion-tracking hardware necessarily increases the cost and complexity of the system and may not fully address the various forms of motion artifact. For instance, when patient motion exceeds the ability of the hardware motion-tracking system to compensate for it, artifacts such as minor capillary distortions and B-scan rotation artifacts may manifest, often appearing in OCT images as discontinuous vessel segments [64, 65]. Alternatively, software-based approaches correct for motion post-acquisition by acquiring and registering multiple volumes [66-69]. Registering multiple volumes involves aligning the volumes to correct for slight variations induced by the head and eye movement of the patient. Therefore, precise registration is highly necessary for ophthalmic imaging to ensure accurate and consistent image quality. 3.2 OCT image registration algorithms There has been a wide range of techniques that have been developed over time to align OCT scans of the same sample. This process of registering OCT volumes is critical for obtaining accurate and consistent results in the analysis and interpretation of the microstructural information captured by the OCT images. Registration of images can be classified into two main categories: image feature-based and volumetric intensity-based which will be further discussed in Sections 3.1.1.and 3.1.2. Numerous algorithms have been developed for registering OCT retinal images. 22 Recently, Pan et al. summarized the existing OCT registration algorithms as well as their advantages and disadvantages in Reference [70]. Table 3.1 presents a comprehensive overview of the most recent and typical registration algorithms used in various applications. These algorithms have been widely used and have demonstrated great success in registering images with high accuracy and precision. The table provides the mode of registration (whether it is 2D or 3D), the method being used and one of the potential applications of the registration algorithm. This information can be useful for selecting the most appropriate registration algorithm for specific applications and understanding the limitations and challenges of each algorithm. It is important to note that all the registration algorithms listed in the table have system dependencies, as each has been developed and optimized for a particular imaging system or FOV. Table 3.1 Summary of the typical registration methods Method Mode Application Volumetric transformation-based [71] 3D Speckle reduction Image feature-based [72] 3D Longitudinal study Volumetric transformation-based [73] 3D Speckle reduction Image feature-based [74] 2D Motion correction Image feature-based [67] 3D Motion correction Volumetric transformation-based [66] 3D Speckle reduction Volumetric transformation-based [68] 3D Motion Correction Image feature-based methods determine transformation parameters by considering distinct anatomical features, which can be selected manually, semi-automatically, or automatically. These methods aim to estimate a point-by-point mapping between the reference and target images by identifying common features. The transformation used is an affine transformation, which involves 23 scaling, translation, shearing, and rotation while maintaining parallel lines. Examples of automatic feature selection algorithms include scale-invariant feature transform (SIFT), binary robust invariant scalable keypoints (BRISK) [75], and speeded up robust features (SURF) [76]. Heisler et al. proposed a 2D motion-correction approach that used features detected in 2D OCTA en-face images to guide a registration algorithm [74]. However, this method discarded most of the depth information in the volumes. Our recent work improved upon this by creating a template-less, 3D registration and averaging methodology by automatically stitching together small, motion-free sub-images [67]. While this method produced excellent 3D renderings, it may fail in some cases due to a lack of adequate en-face features or severe motion. The accuracy of these methods is often dependent on the accuracy of the layer segmentations used for axial alignment or en-face sectioning. Volumetric transformation-based methods aim to improve the similarity between the reference and target volumes [70], and this can be achieved through either normalized cross-correlation (NCC) [77] in the spatial domain or phase-only correlation (POC) [78] in the frequency domain. A recent publication by Cheng et al. proposed a 3D registration method of OCT/OCTA volumes to enhance the SNR and visualization while addressing motion artifacts. However, this method may fail in the case of significant inter-volume motion [66]. Another publication by Kurokawa et al. introduced a software-based approach for the registration of high-resolution AO-OCT retinal images using coarse-to-fine B-scan registration followed by global A-line registration. This approach employs 3D POC and NCC to calculate the displacement and affine transformations to estimate the 3D global coordinates [68]. Although it produces promising results from high-resolution cone mosaic layers, A-line registration may not be reliable for other retinal layers, damaged retinas, or images from systems with lower resolution. 24 3.3 Summary This chapter focuses on the topic of OCT image registration, a crucial process in medical imaging for accurately aligning and merging multiple OCT images of the same area and highlights the challenges and limitations of OCT image registration and the need for continued research in this field to improve the accuracy and efficiency of image registration algorithms and methods. The chapter provides an overview of the various algorithms and methods used for OCT image registration. The algorithms discussed in the chapter include volumetric transformation-based methods and image feature-based methods. These algorithms differ in the way they compare and align the volumes, with intensity-based methods using the intensity values of pixels, and feature-based methods using identifiable features in the images. 25 Chapter 4: System-agnostic 3D registration of OCT volumes 4.1 Motivation In Chapter 3 several registration algorithms that are utilized to solve the issue of motion artifacts in OCT and OCTA images are discussed. Since these methods are in general restricted to the registration of images obtained from particular systems, the methods could not be universally applicable. This chapter focuses on the development of novel a registration method that is independent of any specific specifications and is capable of registering and averaging multiple OCT volumes. The uniqueness of our algorithm lies in its system-agnostic nature, enabling it to register various volumes obtained from different systems having different system parameters. In this study, we iterate upon our previously published works [67, 74] and the algorithm introduced by Kurokawa et al. in Reference [68] to develop a robust and software-based 3D registration algorithm. The proposed method applies coarse registration and affine registrations to correct translational shifts between volumes and affine registration to correct for rotation angle differences, respectively. Our approach demonstrated the creation of high-quality volumes from multiple motion-corrupted data from various sources, hence improving the clinical utility of OCT technology. 4.2 OCT instrumentation and data acquisition In this study, for acquiring many different retinal OCT volumes, we used multiple OCT devices with different system characteristics, including custom-built systems such as a DS-OCT [39, 46] with a high axial resolution, a PD-OCT [79] with high acquisition speed, and a SAO-OCT [80, 81] with a high lateral resolution, as well as a commercially available SS-OCT system (Plex Elite 9000, Carl Zeiss Meditec, Dublin, CA, USA). The specifications of each OCT device are summarized in Table 4.1. The custom-built OCT systems did not have hardware-based motion 26 tracking capabilities, so a visual target was only used for the patients to fixate their eyes during the image acquisition. The subjects were recruited and imaged at the Vancouver General Hospital ECC in accordance with the tenets of Declaration of Helsinki. Each subject was informed of the nature of the study and its implications, and asked to sign a consent form, and before any study procedures or examinations were conducted. Table 4.1 Summary of systems specifications System Type Mode 𝝀𝒄 FWHM FOV Axial resolution Lateral resolution DS-OCT [39, 46] SD OCT OCTA 810 nm 100 nm 6 mm × 6 mm 3.2 µm 9.7 µm PD-OCT [79] SS OCT 1060 nm 70 nm 12 mm × 12 mm 7.0 µm 15.8 µm SAO-OCT [80, 81] AO OCT 1060 nm 70 nm 0.5 mm × 0.5 mm 7.0 µm 3.3 µm Zeiss Plex Elite 9000 SS OCT OCTA 1060 nm 80 nm 6 mm × 6 mm 6.3 µm 20 µm 4.3 Methods 4.3.1 Pre-processing Four pre-processing steps, volume preparation, rough lateral translation correction, axial alignment, and motion removal, are performed prior to the multi-volume registration and averaging process as demonstrated in Figure 4.1. Each of the processing steps is described in detail throughout this section. Figure 4.1 Graphical illustration of multi-volume registration and averaging algorithm 27 Volume preparation: The acquired raw OCT data was processed as following the traditional OCT image processing steps such as wave number resampling [82], numerical dispersion compensation [83], and the Fast Fourier Transform (FFT) process. Axial motion within each volume was corrected by locating the cross-correlation peaks between the reference frame (the middle frame of the volume) and all other frames in the volume in both fast and slow B-scan directions. The volume with the best quality from a set of volumes covering the same retinal area is selected as the reference volume. A reference volume is automatically selected based on a quantitative metric, which considers the level of motion, SNR and image clarity as criteria of the selection. In Reference [68] Kurokawa et al. developed a quantitative measure that incorporates the mentioned criteria. The metric is based on cross-correlating the en-face projections of neighboring fast B-scans. The correlation value and the offset of the peak for each adjacent line are recorded. Large eye motions can result in image distortion and changes in the offset value, leading to a decrease in the correlation value. In addition, noise in the image can also decrease the correlation value and increase the variability of the offset value. To address this, an image metric is defined as Equation 4.1: 𝐶 = 𝑝/𝜎0. 𝜎∆2 (4.1) where 𝑝/ is the mean of the correlation peak heights, 𝜎0 is the standard deviation of peak heights, and 𝜎∆2 is the standard deviation of lateral shifts. The reference volume is selected based on the highest value of 𝐶. Rough lateral translation correction: An approximate translation shift between the selected target and the reference volume is calculated and corrected by a 2D POC [78]. POC calculates the normalized cross-power spectrum using the Fourier transform of the en-face 28 projection images generated from the reference and target volumes. The similarities between the en-face images are estimated by the location of the peak in the inverse Fourier transform of the normalized cross-power spectrum. If the two volumes are identical, the peak appears at zero and the location of the peak shifts proportional to the estimated similarities between the two volumes. The translation amount between the reference and target volume can be calculated using POC as shown in Equation 4.2. (𝑥!, 𝑦!) = 𝑎𝑟𝑔𝑀𝑎𝑥 Jℱ.+ L ℱ{𝑓3(𝑥, 𝑦)} × ℱ∗{𝑓5(𝑥 − 𝑥!, 𝑦 − 𝑦!)}|ℱ{𝑓3(𝑥, 𝑦)} × ℱ∗{𝑓5(𝑥 − 𝑥!, 𝑦 − 𝑦!)}|PQ, (4.2) where ℱand ℱ.+ represent the Fourier transform and inverse Fourier transform of the function 𝑓, respectively, and * denotes the conjugate operation. In addition, 𝑓5 (𝑥 − 𝑥!, 𝑦 − 𝑦!) and 𝑓3(𝑥, 𝑦) denote the en-face projection of the target and reference volumes, respectively, where the target volume is assumed to be translated with respect to the reference volume by 𝑥! and 𝑦!. Note that POC can also be extended to the third dimension and find the rotation and scaling differences. However, only translational shifts with no rotation or scale between the two images were considered in this step for simplicity. In the commercial OCT system, the eye motion is mitigated by the motion tracking system. By detecting and correcting motion, this tracking system can compensate massive saccades and blinks as well as other significant eye movements. A constant feedback control between the OCT machine and tracking device identifies the portion of the data that needs to be reacquired [55], but it constantly shifts the coordinate system of the images as correcting the motion. For the OCT data acquired with the motion tracking on, an additional non-rigid alignment step was added to pre-processing, which takes advantage of Demon’s registration algorithm [40, 41] to compensate the randomized coordinates caused by imaging the motion location several times. Demon’s 29 registration operates on the en-face projection of the reference and the target volumes extracted from the commercial OCT machine to align the target non-rigidly to the reference. The transformation matrix is then applied to the all the layers of the target volume. Since this non-rigid alignment method is feature-based, the more features in the images, the better the registration. Therefore, the superficial projection images obtained from the OCT machine were used. Axial alignment: After correcting the rough lateral translation and non-rigid alignment of the volumes, the process of axial alignment is needed to extend the registration up to 3D. Figure 4.2 presents a representation of this algorithm. The first step is to divide both the target and reference volumes into sub-volumes that are parallel to the slow-scan axis and have a width of M frames. The middle frame of each reference sub-volume is chosen as the template to align all M frames of the corresponding target sub-volume. In the next step, each target frame and the template are further divided into sub-frames along the fast scan direction with a width of P A-lines. Finally, a 2D POC is applied to each sub-frame, and the axial shift is recorded and then applied to the target sub-frames for aligning the template. This process of axial alignment allows the template to be aligned with the target volumes in 3D space, providing an accurate registration of the volumes. 30 Figure 4.2 Illustration of how the axial matching algorithm aligns the axial position of the target volume with the reference volume. In this example M is set to be 10 and P is set to be 5. Motion removal: Each OCT volume has a unique pattern of distortion along the slow B-scan direction due to motion artifacts. In particular, the microsaccadic motion can manifest as abrupt shifts and small capillary distortion in which some vessel segments appear to be fragmented. To detect these artifacts, projection along the depth for each volume was produced and artifacts were automatically detected by calculating the cross-correlation of adjacent B-scans. Each pair of adjacent lines was analyzed in terms of its offset, 𝛥𝑥, where the peak occurs. Since eye motions and noise in the image generally lead to systematic fluctuation in 𝛥𝑥, the peaks and valleys of the 𝛥𝑥 were located. Subsequently, in order to completely remove motion artifacts, the pixel locations corresponding to peaks and valleys, along with three additional pixels on either side, were identified through empirical observation and subsequently removed. This process is illustrated in Figure 4.3. 31 Figure 4.3 Illustration of auto-motion removal algorithm steps 4.3.2 Coarse registration The coarse registration processing is based on the method used in Reference [68] which involves performing 3D POC to determine the displacement of each target frame relative to the best-matching reference frame. To approximately find the displacement of target volumes, each volume was divided across the slow scan direction (y) into evenly spaced sub-volumes N with the width of 3 B-scans. Here 𝑁 should be large enough to represent temporal variation in motion of the volume. However, larger number of sub-volumes elongate the processing time, so it will be chosen depending on the scanning density of the OCT volume for computational efficiency. Finally, using the POC, the relative displacement of each target sub-volume to the reference volume is calculated. Then, linear interpolation is used to estimate the y coordinate displacement of those fast B-scans that were not sampled. 32 The next step is to calculate the displacements in x and y directions with pixel-level precision for each target fast B-scan using the reference and target volumes, and the y displacements. First, the reference volume is divided into sub-volumes proportional to its size. Further, each B-scan in the target volume is mapped to a sub-volume in the reference volume using the y displacements. Then the displacement in the x and y directions for each fast B-scan is calculated using the phase correlation method described in Section 4.3.1. After this step, there are fast B-scans producing inaccurate registration results. It is typically the case for fast B-scans in target volume that do not overlap with those in the reference. These fast B-scans can be detected by comparing their shift amounts to those of adjacent B-scans. If the difference in shift exceeds a tolerance value, which is empirically determined to be between 3 to 5 pixels, this is presumed to be an improper match and removed. Afterwards, interpolation and extrapolation are performed to fill the shifts between the proper shifts. Finally, registered volumes are constructed by shifting each B-scan based on calculated 𝛥𝑥 (translation along the fast scan direction) and 𝛥𝑦 (translation along the slow scan direction). Note that 𝛥𝑧 (axial shift) is supposed to be zero since the volumes have already been aligned axially as described in Section 4.3.1. 4.3.3 Affine registration Following the coarse registration, individual frames in the target volume are occasionally rotated with respect to their corresponding frames in the reference. Consequently, the affine registration compensates for the angle discrepancy between the reference and target volumes by rotating each target to achieve the maximum cross-correlation relative to its corresponding reference. To achieve this, each target and reference volume was projected along the depth and each resulting image was divided into equally spaced strips. Each target strip is then rotated, and its cross-correlation with its corresponding reference strip is calculated. This process is repeated 33 until the rotated target strip and its corresponding reference strip have the highest cross-correlation value. It should be noted that the number of strips depends on the abundance of vascular or cellular features in the projection image. Finally, the rotation angle is applied to all depth positions of each sub-volume. Since the rotation angle is often very small, it could be assumed that the rotation angle to be within the range of ± 1°. Figure 4.4 depicts an illustration of the steps involved in the algorithm. Figure 4.4 The steps of Affine registration: After Coarse registration, the volume is projected along the depth and rotated at various angles ranging from -α° to α° to identify the optimal rotation angle that yields the highest cross-correlation with the reference image. Subsequently, the identified optimal rotation angle is applied to all depth positions. The value of α used in our tested systems was set to 1. 4.4 Results 4.4.1 Qualitative registration performance validation – DS-OCT system Using the high-speed mode of the DS SD-OCT system, a total of 47 OCT volumes were acquired. The comparison between the reference and 47 averaged volumes after registration process for both SVP and DVP are shown in Figure 4.5. The layers were extracted using the automatic segmentation algorithm provided by OCTexplorer 3.8.0 (The Iowa Reference 34 Algorithms, Retinal Image Analysis Lab, Iowa Institute for Biomedical Imaging, Iowa City, IA) [84-88] and subsequent manual corrections were made to the lines that had not been segmented correctly. There is a microsaccadic artifact in the single volume that represents an abrupt change in the vasculature architecture (Figure. 4.5 (A)) which is corrected after more than 40 volumes have been registered and averaged (Figure. 4.5 (C)). Furthermore, there has been an improvement in the overall contrast of the image, which allows more detailed and connected vasculatures to be visualized in DVP (Figure 4.5 (D)) after registration and averaging in comparison to the reference image (Figure 4.5 (B)). Portions of the images enclosed in colored rectangles are magnified at the bottom of each figure to better display the differences between the reference and the averaged volumes. 35 Figure 4.5 Comparison of the single OCT volume with the registered averaged volume acquired from a custom-built dual-spectrometer SD-OCT system. (A, C) SVP of the reference volume and the averaged volume, respectively. The red square in (A) represents the miscorsaccadic motion artifact, while the corresponding green square in (C) represents the corrected motion artifact. (B, D) Deep vasculature of the reference volume and the averaged volume, respectively. Further, blue square indicates an improvement in vasculature contrast and connectivity of the yellow square. Using high-SNR mode of the DS-OCT system a total of 20 OCT/OCTA volumes acquired. A preliminary analysis of the SVP and DVP was carried out using the auto-segmentation algorithm provided by OCTexplorer 3.8.0, and then manual corrections were made to those lines that were not segmented correctly [31-33]. Figures 4.6 (A) – (D) show the SVP and DVP of a reference volume and 20 serially acquired volumes that are registered and averaged. Despite selecting the 36 reference volume based on minimal motion artifact criteria, there are two motion artifacts that manifest as disconnected vessels (Figures 4.6 (A) and (C)), which are corrected by averaging the registered volume (Figures 4.6 (B) and (D)). Furthermore, SVP and DVP layers corresponding to the reference and averaged OCTA volumes are shown in Figures 4.6 (E) – (H). The averaged OCTA volume is acquired by registering the OCT volumes as described in Section 4.3 and then applying the transformation coordinates to the corresponding OCTA volumes and averaging them. A single OCTA volume exhibits microsaccadic motion artifacts in the form of vertical bright lines, which correspond to eye movements. Figures 4.6 (D) and (H) demonstrate the motion artifact removal and the improvement in the overall contrast and visualization of the vasculatures in comparison to Figures 4.6 (C) and (G). 37 Figure 4.6 Illustration of the OCT/OCTA volumes acquired using the high-SNR mode of the custom-built dual spectrometer system. (A, B) Comparison between the single reference and 20 registered and averaged OCT volumes for the SVP. (C, D) Comparison between the single reference and averaged OCT volumes for the DVP. The red squares correspond to the same regions in the OCTA of the reference and averaged volumes, which have been magnified at the bottom for better visibility. (E, F) SVP of the corresponding single and averaged OCTA volumes. The red square in (E) corresponds to the motion artifact which is corrected in (F). Further, yellow squares show the improvement in contrast of the SVP. (G, H) DVP layer of the single and averaged OCTA volumes. Green square corresponds to the microsaccadic motion artifact in the single volume which is corrected after registering and averaging multiple volumes. 38 4.4.2 Qualitative registration performance validation – PD-OCT system A total of 11 volumes were acquired using the PD-OCT which enables the acquisition of DOPU contrast. Figures 4.7 (A) and (B) illustrate the en-face mean projection of nerve fiber layer (NFL) of the reference and registered averaged volumes, respectively. It involves segmenting the top of the NFL using OCTexplorer automatic pre-segmentation feature and averaging the pixels from the segmented line to twenty pixels below the segmented line. To facilitate the comparison between the two figures, three regions of each figure are enclosed in colored squares and magnified below each figure. The red arrow in Figure 4.7 (A) points to an area of motion artifacts, which has been corrected in Figure 4.7 (B). Figures 4.7 (C) and (D) illustrate the en-face mean projection of the RPE layer of the reference and registered averaged DOPU volumes, respectively. The segmented line has been acquired by segmenting the top of the RPE layer using the automatic pre-segmentation feature of the OCTexplorer and then averaging the pixels from the segmented line to 40 pixels below the segmented line. The comparison of the two images shows a considerable improvement in the DOPU signal after multi-volume registration and averaging and the contrast of the image has been noticeably improved. Figures 4.7 (E) and (F) show the corresponding B-scans of the reference and averaged volumes overlayed with the DOPU signal, respectively. In addition, the DOPU contrast in Figure 4.7 (F) appears to be smoother and more connected than the signal in Figure 4.7 (E). 39 Figure 4.7 Comparison of reference and registered averaged volumes acquired from PD-OCT system. (A) NFL of the single volume. Three regions are enclosed in the colored squares in the figure and magnified below the image. The red arrow shows the motion artifact. (B) NFL of the averaged volume corresponds to the NFL of the reference volume. The same regions are marked on the figure and magnified below the image. The red arrow points to the corresponding motion artifact location in the reference volume which is corrected in the averaged volume. (C) Representative B-scan of the single volume. (D) Representative B-scan of the averaged volume corresponds to the B-scan in (C). (E) Representative B-scan of the single volume overlayed with the DOPU signal. (F) Representative B-scan of the averaged volume overlayed with the DOPU signal. 40 4.4.3 Qualitative registration performance validation – SAO-OCT system Using the SAO-OCT system, 88 OCT volumes were acquired sequentially. First, the volumes were registered according to the methods described in Sections 4.3.2 and 4.3.3. For affine registration, each volume was projected along the depth, divided into ten sub-volumes along the slow B-scan direction, and rotated individually. Nevertheless, the small rotation angle combined with this division resulted in some visible vertical lines after the images were registered and averaged. Since the rotation angle was small, each image was up-sampled by a factor of 2, rotated each sub-image, and then down-sampled each image. Artifacts were prevented from appearing in the images after they had been aligned by this technique. Figure 4.8 demonstrates the results of registering and averaging the volumes using the method described above and in Section 4.3.3. Although the single reference volume shown in Figure 4.8 (A) is free of motion artifacts, there are still distortions resulting from tremor motion that can be seen in the irregularity of the cone mosaic. On the other hand, the cone mosaics (Figure 4.8 (B)) extracted from the averaged volume appear more regular than the ones from the reference volume. 41 Figure 4.8 Performance of the 3D registration algorithm on the OCT volumes acquired using the custom-built SAO-OCT system. (A, B) Illustration of cone mosaic layer for the reference and registered averaged volume. The irregularity of the photoreceptors has been corrected after the 3D registration. (C, D) Projection of the RPE layer for the reference and the averaged volumes respectively. (E, F) Projection of the DVP for both reference volume and the averaged volume which indicates the improvement of the contrast and therefore connectivity of the vasculatures, and reduction in the speckle noise, after registration and averaging. Using the 3D registration algorithm, more fine features of the inner and outer retina are resolved after averaging. Figures 4.8 (C) and (D) show a comparison between the en-face mean projection of the RPE layer of the reference volume and the corresponding layer in the averaged volume. Figures 4.8 (E) and (F) illustrate the performance of the 3D registration algorithm in visualizing the DVP after averaging. The resultant image has better contrast, more connected 42 vasculature and the speckle noise has been significantly reduced. To validate the performance of the affine registration, a comparison of the reference volume with the coarse- registered volume and the fully registered volume has been made. Figures 4.9 (A) and (B) show the overlaying of reference volume and the coarse-registered volume and the fully registered volume, respectively. The green and magenta colors correspond to the reference and averaged volumes, respectively, and the white color illustrates the pixels from the two volumes that are exactly matched. Red arrows indicate portions of two images that have been better aligned with the reference volume after affine registration. Figure 4.9 Illustration of the performance of affine registration in improving the registration and averaging result. (A) Shows the overlay of the reference volume and the coarse-registered volume. (B) shows the overlay of the reference volume and the fully registered volume (coarse and affine registered). Green and magenta show the reference volume and the averaged volume, respectively. The white pixels correspond to the matching pixels of two volumes. It is evident from the image that the number of matching pixels has increased after the affine registration, which indicates that the two volumes align more effectively after full registration. 43 4.4.4 Qualitative registration performance validation – commercialized OCT device A total of 10 OCT/OCTA volumes were acquired sequentially using the commercial OCT system with the motion tracking system from the right eye of a patient diagnosed with the central retinal vein occlusion (CRVO). The volumes were registered using our 3D registration algorithm which utilizes both the coarse and affine registration techniques, along with the non-rigid registration to compensate the randomized coordinate as discussed earlier in Section 4.3.1. Non-rigid registration step incorporates the Demon's non-rigid registration and processes it after the rough lateral translation alignment. Adding this step will compensate for the random coordinates generated by the motion tracking system, resulting in a better alignment of the volumes. Figure 4.10 illustrates the comparison between the single volume and the registered averaged volume for the SVP and DVP of the OCT and OCTA volumes. Figures 4.10 (A) and (B) represent the superficial layers of the reference and registered averaged OCT volumes. The regions enclosed in the red squares are magnified at the bottom of the image, demonstrating a clear visualization of vasculature after registration, and averaging. Figures 4.10 (C) and (D) show the DVP of the single reference volume and registered averaged volume, respectively. As shown at the bottom of the image, the areas enclosed in the green squares are magnified demonstrating an improvement in the image brightness following the registration and averaging process. Figures 4.10 (E) and (F) illustrate the performance of SVP registration for the OCTA volumes. One of the features of the CRVO patients is that there are some hyper-reflective areas in OCTA images especially along the top of the SVP as shown by yellow arrows, which has better contrast and less background noise after registration and averaging. Figures 4.10 (G) and (H) illustrate the comparison between the DVP of the reference and the registered averaged volume. The regions enclosed in the colored 44 squares are magnified at the bottom, showing brighter and more connected vasculature after registration, and averaging compared to the reference volume as pointed by the red arrows. Figure 4.10 Illustration of the OCT/OCTA volumes acquired using commercialized machine (Zeiss Plex Elite 9000). (A, B) Comparison between the single reference and 10 registered and averaged OCT volumes for the SVP. (C, D) Comparison between the single reference and averaged OCT volumes for the DVP. (E, F) SVP of the corresponding single and averaged OCTA volumes. (G, H) DVP of the single and averaged OCTA volumes. The regions of the images enclosed in the colored squared are magnified at the bottom of the image for better visualization and comparison. 4.4.5 Quantitative registration performance evaluation Currently, there is no gold standard method for evaluating the performance of image registration algorithms. which makes it difficult to measure the accuracy of the registration. A qualitative analysis of the projection image of the different layers of the single and averaged volume is an effective method of evaluating the performance of a registration algorithm. In addition, there are several reliable quantitative methods that may be used to approximate the 45 registration performance. The improvement of the image quality was evaluated quantitatively using two metrics: Mean squared error (MSE) and multiscale structural similarity index measure (MS-SSIM). Both metrics evaluate the quality of a single image in comparison to the averaged high-quality image. MSE is defined in Equation 4.3, 𝑀𝑆𝐸 = T 1𝑋𝑌𝑍: X𝐼678 − 𝐼9Y":,<,= , (4.3) where X, Y and Z are the dimensions of the volume, 𝐼678 and 𝐼9 represent the pixel intensity of the averaged and reference volumes, respectively [68]. Structural similarity index measure (SSIM) measures the quality of the registered image by quantifying the structural similarity between the registered image and the reference image. SSIM is defined as: 𝑆𝑆𝐼𝑀(𝑥, 𝑦) = X2𝜇2𝜇> + 𝑐+YX2𝜎2> + 𝑐"YX𝜇2" + 𝜇>" + 𝑐+YX𝜎2" + 𝜎>" + 𝑐"Y , (4.4) where 𝑥 and 𝑦 represent the en-face mean projection of the reference volume and the registered averaged volume along the depth, respectively. 𝜇 , 𝜎" and 𝜎 are the average, variance, and covariance, respectively. The terms 𝑐+ and 𝑐" are two small constants added to avoid instability when 𝜇2" + 𝜇>" and 𝜎2" + 𝜎>" are equal to zero [74]. MATLAB (R2021a; MathWorks) built-in function, multissim3, was used which calculates the MS-SSIM by combining the SSIM index of several versions of the volume at various scales. To further evaluate the performance of the proposed registration algorithm, MSE and MS-SSIM were calculated for each final averaged registered volume. Figure 4.10 illustrates how the MSE (orange) and MS-SSIM (blue) values improve after increasing the number of averaged volumes. Further, Table 4.2 summarizes the initial and final values of each graph. Figure 4.11 (A) and the first row of the table show the MSE and MS-SSIM for the 47 registered OCT volumes 46 acquired from high-speed mode of the DS-OCT system. The MSE value between the reference and one registered volume is 0.4090, while this value gradually decreases to 0.0195 as more registered volumes are averaged. In addition, MS-SSIM values have shown an increasing trend from 0.0748 for one registered image to 0.9765 for 47 registered and averaged images. Figure 4.11 (B) and the second row of the table illustrate the MSE and MS-SSIM of the 12 OCT volumes acquired from the high-SNR mode of the DS -OCT system. The MSE value decreases from 0.4458 for one registered volume to 0.0484 for 12 registered and averaged volumes. Also, the MS-SSIM value increases from 0.2019 to 0.9454, indicating the accuracy of the proposed algorithm. Figure 4.11 (C) and the third row of the table illustrate the MSE and MS-SSIM of the 88 registered OCT volumes acquired from the SAO-OCT system. After averaging 88 volumes, MSE values decreased from 0.1705 to 0.0577 and MS-SSIM values increased from 0.5931 to 0.7883. Figure 4.11 (D) and the fourth row of the table show the MSE and MS-SSIM of the 10 registered OCT volumes acquired from the PD-OCT system. The MSE value is determined by comparing one registered volume to 10 registered and averaged volumes, starting from 0.5263 and decreasing to 0.0590. MS-SSIM, on the other hand, starts at 0.0503 and reaches 0.9425 after averaging 10 registered volumes. Figure 4.11 (E) and the fifth row of the table show the MSE and MS-SSIM graphs for 10 registered OCT volumes acquired using the commercialized OCT system from a pathological eye as described in Section 3.4. The MSE value between the reference and one registered volume is 0.3989. This value gradually decreases to 0.1478 as more volumes are registered and averaged. Moreover, the MS-SSIM value increases from 0.0660 to 0.5879. In short, by averaging more registered volumes, the MS-SSIM and MSE graphs show an increasing trend and a decreasing trend, respectively, indicating a significant increase in the similarity between the reference volume and the averaged volume. 47 Table 4.2. Initial and Final values of the MS-SSIM and MSE for different systems MS-SSIM MSE System Initial Value Final value Initial Value Final Value DS-OCT (High-speed) 0.0748 0.9765 0.4090 0.0195 DS-OCT (High-SNR) 0.2019 0.9454 0.4458 0.0484 SAO-OCT 0.5931 0.7883 0.1705 0.0577 PD-OCT 0.0503 0.9425 0.5263 0.0590 Zeiss Plex Elite 9000 0.0660 0.5879 0.3989 0.1478 48 Figure 4.11 Numerical evaluation of the registration performance using MSE and MS-SSIM metrics with the increase of averaging numbers. (A) Shows MSE and MS-SSIM evaluation of the OCT volumes acquired from the dual spectrometer system using high speed mode. (B) Illustrates MSE and MS-SSIM evaluation of the OCT volumes acquired from the high SNR mode of the dual spectrometer system. (C) Shows MSE and MS-SSIM evaluation of the OCT volumes acquired from SAO-OCT system. (D) Illustrates MSE and MS-SSIM evaluation of the OCT volumes acquired from the custom-built wide-FOV PD-OCT system. (E) Shows MSE and MS-SSIM evaluation metrics of the OCT volumes acquired from commercialized OCT system (Zeiss Plex Elite 9000). 49 4.5 Discussion In this study, an effective and robust 3D retinal OCT registration algorithm has been introduced which can be used for correcting the various motion artifacts as well as improve the image contrast as averaging multiple volumes after the registration process. The registration process was performed in three sequential steps: pre-processing, coarse registration, and affine registration. In the pre-processing, the reference volume was automatically selected and used to align the rest of the volumes. Further, an axial matching algorithm was deployed to align the volumes axially along the depth which was followed by a motion removal algorithm to remove the motion artifacts in the volumes automatically. Coarse registration was implemented to align the position of the B-scans in the volumes laterally which was followed by affine registration algorithm to correct for the fine details in the images and improve their alignments. The proposed algorithm has demonstrated several benefits of 3D registration and averaging of serially acquired OCT images. Some of its advantages are included but not limited to 1) The auto-motion-removal approach has facilitated detection and removal of the slow drift motion as well as microsaccadic motion along the slow scan direction. An auto-motion-removal algorithm was introduced in our previous work [67], which enabled detection of motion artifacts from OCTA en-face images. However, OCTA volumes do not always exist. As a result, we have developed an alternative auto-motion-removal algorithm, as described in Section 4.3.1, which is capable of robustly detecting motion from OCT en-face images; 2) Clinicians and researchers benefit greatly from high resolution images of macular microvasculature. However, the quality of single volume OCT image is not capable of providing meaningful data for accurate analysis due to low vessel contrast and motion artifacts. The retinal plexus visualization and contrast has significantly improved after multi-volume registration and averaging using coarse and affine registrations 50 compared to the single volume image. Moreover, the volumetric nature of the algorithm is capable of producing high-contrast images of both the OCT B-scans and the en-face scans. We quantified this improvement using two metrics, namely, MSE and MS-SSIM; 3) The developed algorithm is not limited to registering only OCT volumes and it can be applied to any other OCT extensions such as OCTA volumes or DOPU signals; 4) The introduced method is not dependent on any OCT system specifications, such as wavelength, full width half maximum (FWHM), axial and lateral resolutions, therefore it can be applied to OCT volumes acquired from any custom-built or commercialized systems. Several key examples were presented in order to illustrate the performance of the algorithm. Our algorithm has proven to be robust enough to provide reliable registration for most subjects, including patients with retinal diseases. Despite the ability of our algorithm to enhance retinal plexus visualization by registering and averaging multiple sequentially acquired OCT volumes, we acknowledge several limitations of this work. First, in our method we assume that the acquisition time of each fast B-scan is short enough to contain negligible head and eye motion. Second, the final registered averaged volume has a FOV that corresponds to the FOV of the reference volume. Multiple reference volumes can be used to address this limitation. According to the method used for selecting the reference volume, multiple reference volumes can be selected by ranking them from high-quality to low-quality. It is only different that instead of choosing the best volume as the reference, the best three or four volumes are selected as the references and the results acquired from different references are combined to create a volume with wider FOV. Third, despite the accurate segmentation and manual corrections of the segmentation lines, some projection artifacts associated with superficial vessels remain in the final averaged volumes, which can obscure deeper vessels. The future studies should consider the use of 3D projection artifact compensation methods such as projection 51 resolved OCTA [89], in order to compensate for these projection artifacts and improve the 3D visualization of the retinal microvasculature. Lastly, as mentioned earlier in Section 4.5 there is no gold standard method to evaluate the performance of the registration algorithm by validating whether there is any misalignment in the final image or not and current evaluation methods can only validate contrast and SNR improvement and can indicate how much the two images are similar to each other by using statistical measurements. 4.6 Summary In ophthalmic clinics, OCT is widely used as a non-invasive in-vivo optical imaging modality for 3D human retina imaging. However, patient-induced motion artifacts can severely degrade the image quality in the single OCT volume. This work has addressed this issue by iterating upon previous works to develop a 3D registration algorithm for improving the retinal OCT image quality. The multi-volume registered data followed by averaging offers significantly enhance visualization of the retinal microvascular network as well as retinal morphological features. Further, we have validated that our methodology is not limited to the specific OCT modality but applicable to any OCT data acquired using different OCT systems with different system specifications. 52 Chapter 5: Clinical applications of image registration 5.1 Motivation As the blood flow through capillaries changes periodically for a few seconds or minutes, vasomotion occurs as a result of periodic contractions of precapillary sphincters and terminus arterioles. This variability in blood flow is considered normal in healthy individuals and is an important characteristic in determining the autoregulatory capacity of capillaries to respond to changing local oxygen and metabolic demands. In other words, the ability of capillaries to regulate their blood flow in response to changes in metabolic and oxygen needs is heavily dependent on this variation in perfusion [90, 91]. It has long been believed that changes in ocular perfusion are associated with the pathogenesis of glaucoma, however the details remain unknown [16]. Glaucoma is a complicated condition that affects the optic nerve and causes the gradual loss of retinal ganglion cells, leading to vision loss and potentially total blindness. Early detection of structural damage is crucial for diagnosing and managing glaucoma. In the past, evaluation of the optic nerve head and the surrounding retinal layer was based on clinical observation. However, the use of ocular imaging has now added a quantitative, objective element to structural evaluation [15]. We propose that variation in sequentially acquired OCTA volumes can be measured to estimate retinal perfusion heterogeneity. The methods outlined in the preceding chapter are used here to ensure that successively collected OCTA images are properly registered so that the pixel-wise variation signal could be extracted. The goal of this study is to define and validate the use of OCTA in the assessment of depth-resolved perfusion heterogeneity, as a marker of vasomotor dysregulation, in patients with glaucoma and healthy subjects. 53 5.2 OCTA imaging protocol All patients were imaged using the PLEX Elite 9000 OCTA (Zeiss Meditec, Dublin, CA). The product specifications were discussed in Table 4.1. Ten 3 × 3 mm2 en-face images were acquired and SVP and the DVP were segmented using the automatic layer segmentation method. Images were obtained using the built-in fixation-tracking function, which recognizes and corrects significant microsaccade motion artifacts. In order to compensate for the remaining distortions in the image, the rigid and non-rigid registration methods previously discussed were applied. The entire data set was manually reviewed after acquisition in order to exclude images and sub-images displaying artifacts that might obscure the pixel intensity coefficient of variation calculation perfusion heterogeneity signal. All patients provided written informed consents following approval by the research ethics board. A total of 16 control patients and 36 patients with glaucomatous optic neuropathy were included in this study. The exclusion criteria included any history of vascular or ophthalmic disease or treatment that has been known to affect retinal microcirculation. Table 5.1 provides demographics information on all study participants. Table 5.1 Demographic of study participants System Eyes (n) Age (mean) Gender (male: female) Control Patients 16 32.9 11:5 Glaucoma Patients 36 65.6 16:20 5.3 Methods 5.3.1 OCTA image registration and averaging MATLAB (R2021a; MathWorks) was used to analyze the 10 consecutive SVP and DVP images of each eye acquired from the acquisition device. A 3D stack of images was constructed from each set of ten images in order to analyze temporal variation. To compensate directly for 54 significant motion artifacts, the OCTA technology utilizes hardware-based motion tracking. However, some motion artifacts are still present in the images that are produced. In order to correct these remaining artifacts, a simplified version of the methodology in Reference [67] (only lateral rigid and non-rigid registration was employed, not axial registration) was utilized. In order to obtain an image with high SNR and contrast-to-noise ratio for use as input for a neural network-based vessel segmentation technique [92], the stack of registered images was averaged. This framework results in high-quality OCTA images that contain well-traced retinal vessels. 5.3.2 Vessel segmentation using Deep Neural Network (DNN) En-face images were acquired sequentially and registered to align with each other. Then, a DNN was employed to segment the vessels and differentiate the vascular signal from the surrounding background noise. The DNN network for vessel segmentation utilized a variation of the U-Net [93] architecture and was adapted for two classes: vessel and background. To train the network, two datasets were used, consisting of 29 images with a 2 × 2 mm2 FOV acquired with a prototype swept-source OCT instrument and 47 images with a 3 × 3 mm2 FOV acquired with a commercial spectral-domain OCT instrument. Each OCT-A image was manually segmented using a Surface Pro tablet by one trained rater and verified and accepted by two additional trained raters. These steps were crucial for training the neural network with accurate data to achieve optimal vessel segmentation results [94]. From an averaged grayscale OCTA image as the input, the neural network generates a "probability map" of the same size, with each pixel representing a value between 0 and 1, where 0 indicates a low likelihood of a pixel being a blood vessel and 1 indicates a high likelihood of a pixel being a vessel. Probability maps were thresholded at 0.5, which corresponds to 50% 55 confidence that a pixel represents a blood vessel. To reduce background noise, the registered image stack was multiplied by the thresholded image before being utilized as a mask. 5.3.3 Coefficient of Variation (CoV) calculation The CoV computation is the last processing step to estimate the retinal capillary perfusion heterogeneity. CoV assesses the pixel-level variability of vascular signal between the serially acquired OCTA images and is defined as the ratio of the standard deviation to the mean of a corresponding input vector [90]. Each CoV calculation's input vector is an array holding the 10 intensities of pixels in the same places on the OCTA images for each of the 10 acquired and registered en-face images. After that, a color map was used to depict the calculated CoV values. A closer look at the final CoV image produced by the pipeline of registration, averaging, and DNN segmentation can be found in Figure 5.1. Figures 5.1 (A-D) depict the following: the reference image, 10 registered and averaged images, the image segmented using DNN, and the map of CoV of the SVP in a 3 × 3 mm2 FOV. Similarly, Figures 5.1 (E-H) show the reference image, averaged image, DNN segmented image, and CoV map of the DVP in the same 3 × 3 mm2 FOV. 56 Figure 5.1 Pipeline of registration, averaging, segmentation and CoV processing for 10 OCTA images acquired from a Glaucomatous eye. (A) SVP layer extracted from a 3 × 3 mm2 reference image, (B) SVP of the averaged registered volume, (C) DNN segmented SVP, (D) CoV color map of the SVP. The color map shows from no variation (blue) to high variation (red), (E) DVP layer extracted from a 3 × 3 mm2 reference image, (F) DVP of the averaged registered volume, (G) DNN segmented DVP, (H) CoV color map of the DVP. The color map shows from no variation (blue) to high variation (red). 5.4 Results 5.4.1 Clinical outcome measure A total of 52 eyes were imaged, of which 11 control eyes and 11 glaucoma eyes (7 open-angle glaucoma, 4 normal-tension glaucoma) with no significant motion artifacts were analyzed. Table 5.2 summarizes the CoV information obtained from different layers of all 11 control eyes and 11 glaucomatous eyes. According to the table, the mean CoV in the DVP was significantly higher than in the SVP for both the control and glaucoma groups and the mean SVP and DVP were significantly higher in glaucomatous eyes than in controls. Figure 5.2 shows a comparison between 57 a control eye and a glaucomatous eye. The averaged image, binary mask, and CoV map for the SVP of the glaucomatous eye are presented in Figures 5.2 (A-C), while Figures 5.2 (D-F) depict the same for the DVP of the same eye. Additionally, Figures 5.2 (G-I) display the averaged image, binary mask, and CoV map of the SVP of the control eye, and Figures 5.2 (J-L) depict the same for the DVP of the control eye. These results demonstrate that the CoV in DVP is higher than SVP for both control and glaucomatous eyes, and that the CoV is higher for glaucomatous eyes than for control eyes. Overall CoV in DVP is higher than in SVP in both control and glaucoma eyes, and second, the CoV is higher in Glaucoma eyes than in control eyes which supports the quantitative results. Table 5.2 Comparison of average and standard deviation of CoV for different layers of healthy and glaucomatous eyes Patient Type Average ± standard deviation All layers CoV SVP CoV DVP CoV Healthy 0.207 ± 0.015 0.237 ± 0.018 0.268 ± 0.021 Glaucoma 0.287 ± 0.040 0.312 ± 0.037 0.449 ± 0.098 58 Figure 5.2 Representative comparison between two Glacouma and control eyes for SVP and DVP layers. (A-C) Illustrate the registered avaeraged image, binary mask and CoV map of the SVP of a glaucoamctouas eye, respectively. (D-F) Represent the registered averaged image, binarized image and the CoV map of the DVP the same glaucomatous eye, respectively. (G-I) Show the registered avaeraged image, binary mask and CoV map of the SVP of a healthy eye, respectively. (J-L) Illuustrate the registered averaged image, binarized image and the CoV map of the DVP the same control eye, respectively 59 5.5 Summary In this chapter, an analysis of the retinal perfusion heterogeneity was explored. For the analysis, serially acquired OCTA en-face images are registered and averaged. For the averaged OCTA en-face image, the retinal vasculature is extracted using the DNN-based vessel segmentation and used for the quantification of the retinal perfusion heterogeneity. The methods employed in this study are subsequently tested in real clinical settings involving glaucomatous eyes. The findings reveal a significant increase in retinal perfusion heterogeneity in glaucomatous eyes compared to control eyes. 60 Chapter 6: Potential Future work and Conclusion 6.1 Future Direction 6.1.1 High-speed system-agnostic 3D volume registration with sub-pixel precision In Chapter 4 of this thesis, a framework was introduced for conducting 3D image registration. The framework involved selecting a reference volume and a target volume and applying shifts with pixel-level precision to the target volume in order to align it with the reference volume. However, an improved version of the algorithm utilizes an interpolation algorithm to align the target volumes with sub-pixel level precision. This advancement allows for the combination of data from multiple volumes at a higher resolution. In addition, Initially, the algorithm was implemented using MATLAB (R2021a; MathWorks), but in order to speed-up the process, we later implemented the algorithm on Python 3. However, there are additional techniques that can be explored to further decrease the registration time, such as implementation on a GPU or multiple GPUs. These alternatives are worth considering to further optimize the registration process. 6.1.2 OCT-based 3D perfusion heterogeneity analysis In Chapter 5 of this thesis, we examined retinal perfusion heterogeneity from OCTA images extracted from a commercialized system using our previously published 2D registration algorithm [67]. We chose to use the 2D registration algorithm for two reasons: It was faster to register, and effective heterogeneity analysis required precise segmentation, which could be challenging at times. For the analysis, we preferred to use the en-face images extracted from the commercialized machine. However, as an improvement, once the registration algorithm provides high-speed performance, we can use it to register 3D volumes acquired from custom-built systems. In Chapter 4, we introduced the custom-built dual-spectrometer system that can acquire data at a faster speed, higher resolution, and higher SNR. Once the data has been acquired, it will 61 be processed using the 3D image registration method from Chapter 4 and the CoV calculation pipeline introduced in Sections 5.3.1-5.3.3 to produce the results. Figure 6.1 gives an overview of the CoV processing pipeline. A template image is selected, and multiple images are registered and averaged to produce a high-quality image. A binary mask of the high-quality image and registered strips is then used to produce the CoV map. However, there are strip artifacts on the CoV map resulting from averaging the B-scans over the volume non-evenly. This mismatching of B-scans causes line artifacts in the averaged volumes and, as a result, more severe line artifacts in the CoV map. To prevent this from happening, we found empirically that at least five B-scans per location need to be averaged. Figure 6.1 Processing pipeline results of image registration, averaging and CoV calculations on OCTA images. In addition to the CoV results of OCTA volumes, performing the CoV analysis on OCT image data will be beneficial. as the temporal sequence of the OCT signals can be used to visualize and quantify the tissue dynamics [95]. The performance of this custom-built system and the application of the CoV pipeline on OCT data will provide high quality tissue visualization at a high speed. Figure 6.2 illustrates the processing pipeline results of image registration, averaging and CoV calculation on OCT images. 62 Figure 6.2 Processing pipeline results of image registration, averaging and CoV calculations on OCT images. 6.2 Conclusion This thesis presents a framework that can be applied across different OCT/OCTA systems with varying specifications, to achieve accurate 3D registration of volumes. The framework involves a range of techniques including pre-processing, coarse registration, and affine registration, which work together to ensure high accuracy in the registration process. 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Thesis/Dissertation
eng
Biomedical Engineering
Vancouver : University of British Columbia Library
Attribution-NoDerivatives 4.0 International
Graduate
Ju, Myeong Jin
Functional 3D retinal imaging using OCT technology and a novel, system-agnostic registration algorithm
Text