Awasthi Navchetan, Kumar Kalva Sandeep, Pramanik Manojit, Yalavarthy Phaneendra K
Department of Computational and Data Sciences, Indian Institute of Science, Bangalore 560012, India.
School of Chemical and Biomedical Engineering, Nanyang Technological University, 637459, Singapore.
Biomed Opt Express. 2021 Feb 8;12(3):1320-1338. doi: 10.1364/BOE.415182. eCollection 2021 Mar 1.
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.
用于解决具有有限噪声数据的光声层析成像不适定逆问题的重建方法本质上是迭代的，以提供精确的解。这些方法的性能受到光声数据中噪声水平的高度影响。本文提出了一种基于奇异值分解（SVD）的即插即用先验方法来解决光声逆问题，以增强对数据中噪声的鲁棒性。与全变差正则化、基追踪去卷积和基于 Lanczos Tikhonov 的正则化相比，该方法表现更优，并且在存在噪声数据的情况下性能有所提升。数值和实验案例表明，与现有方法相比，重建图像的信噪比提高可达8.1 dB，均方根误差降低67.98%。
