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Investigating signal denoising and iterative reconstruction algorithms in photoacoustic tomography

University of British Columbia

Photoacoustic tomography (PAT) is a promising biomedical imaging modality that achieves strong optical contrast and high ultrasound resolution. This technique is based on the photoacoustic (PA) effect which refers to illuminating the tissue by a nanosecond pulsed laser and generating acoustic waves by thermoelastic expansion. By detecting the PA waves, the initial pressure distribution that corresponds to the optical absorption map can be obtained by a reconstruction algorithm. In the linear array transducer based data acquisition system, the PA signals are contaminated with various noises. Also, the reconstruction suffers from artifacts and missing structures due to the limited detection view. We aim to reduce the effect of noise by a denoising preprocessing. The PAT system with a linear array transducer and a parallel data acquisition system (DAQ) has prominent band-shaped noise due to signal interference. The band-shaped noise is treated as a low-rank matrix, and the pure PA signal is treated as a sparse matrix, respectively. Robust principal component analysis (RPCA) algorithm is applied to extract the pure PA signal from the noise contaminated PA measurement. The RPCA approach is conducted on experiment data of different samples. The denoising results are compared with several methods and RPCA is shown to outperform the other methods. It is demonstrated that RPCA is promising in reducing the background noise in PA image reconstruction. We also aim to improve the iterative reconstruction. The variance reduced stochastic gradient descent (VR-SGD) algorithm is implemented in PAT reconstruction. A new forward projection matrix is also developed to more accurately match with the measurement data. Using different evaluation criteria, such as peak signal-to-noise ratio (PSNR), relative root-mean-square of reconstruction error (RRMSE) and line profile comparisons, the reconstructions from various iterative algorithms are compared. The advantages of VR-SGD are demonstrated on both simulation and experimental data. Our results indicate that VR-SGD in combination with the accurate projection matrix can lead to improved reconstruction in a small number of iterations. RPCA denoising and VR-SGD iterative reconstruction have been implemented in PAT. Our results show that RPCA and VR-SGD are promising approaches to improve the image reconstruction quality in PAT.

Text

2017-08-17

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/62689

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/