Abstract
The reconstruction methods for solving the ill-posed inverse problem of photoacoustic tomography with limited noisy data are iterative in nature to provide accurate solutions. These methods performance is highly affected by the noise level in the photoacoustic data. A singular value decomposition (SVD) based plug and play priors method for solving photoacoustic inverse problem was proposed in this work to provide robustness to noise in the data. The method was shown to be superior as compared to total variation regularization, basis pursuit deconvolution and Lanczos Tikhonov based regularization and provided improved performance in case of noisy data. The numerical and experimental cases show that the improvement can be as high as 8.1 dB in signal to noise ratio of the reconstructed image and 67.98% in root mean square error in comparison to the state of the art methods.
Highlights
Photoacoustic tomography (PAT) is a hybrid imaging modality providing optical absorption contrast at high ultrasonic resolution [1,2,3,4]
The reconstruction using Basis Pursuit Deconvolution (BPD) and Total Variation are shown in Fig. 4(b) and (c) respectively for the same signal to noise ratio (SNR) in the data
For the SNR of 40 dB, the proposed method gave an improvement of 26.65 %, 14.15 % and 3.34 % in Root Mean Square Error (RMSE), Contrast to Noise Ratio (CNR) and Pearson Correlation (PC) as compared to the other reconstruction methods
Summary
Photoacoustic tomography (PAT) is a hybrid imaging modality providing optical absorption contrast at high ultrasonic resolution [1,2,3,4]. It involves usage of a pulsed laser (temporal duration in nanoseconds) in the range of 600 - 1000 nm (near-infrared (NIR)) for irradiating the tissue. The photoacoustic (PA) waves are generated because of light absorption and propagate inside the tissue, to be acquired by the transducers placed at the boundary of tissue. The strength of PAT lies in the fact that the attenuation and scattering of acoustic waves is far less compared to light propagation and the propagation inside the tissue without significant scattering can be achieved. Photoacoustic imaging has been widely used for revealing functional and structural information in clinical and pre-clinical applications, non-invasive monitoring of traumatic brain injury [5], oncology [6,7], pathology, molecular imaging [3] and enables deep tissue monitoring because of higher light penetration in the NIR-window [8]
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