Generative model for sparse photoacoustic tomography artifact removal

Guijun Wang; Yanan Hu; Gang Hu; Hongyu Zhang; Qiegen Liu; Xianlin Song

doi:10.1117/12.2683128

18 July 2023 Generative model for sparse photoacoustic tomography artifact removal

Guijun Wang, Yanan Hu, Gang Hu, Hongyu Zhang, Qiegen Liu, Xianlin Song

Proceedings Volume 12745, Sixteenth International Conference on Photonics and Imaging in Biology and Medicine (PIBM 2023); 1274503 (2023) https://doi.org/10.1117/12.2683128
Event: Sixteenth International Conference on Photonics and Imaging in Biology and Medicine (PIBM 2023), 2023, Haikou, China

Abstract

Sparse reconstruction in photoacoustic tomography has always faced the problem of artifacts. To address this issue, a diffusion model-based method for sparse data reconstruction in photoacoustic tomography was proposed. During the training phase, the gradient of the probability density of the image was learned as the data prior by adding noise and denoising at each step. During the testing phase, ultrasonic signals are generated by illuminating with pulsed laser and acquired by ultrasonic transducers surrounding the object, which was implemented using the k-Wave toolbox. The reconstructed image was finally obtained by reserve-time Stochastic Differential Equation (SDE). Experimental results on vascular data show that the proposed algorithm can effectively remove artifacts and improve image quality compared with conventional reconstruction methods under 32 and 64 detectors, respectively.

Citation Download Citation

Guijun Wang, Yanan Hu, Gang Hu, Hongyu Zhang, Qiegen Liu, and Xianlin Song "Generative model for sparse photoacoustic tomography artifact removal", Proc. SPIE 12745, Sixteenth International Conference on Photonics and Imaging in Biology and Medicine (PIBM 2023), 1274503 (18 July 2023); https://doi.org/10.1117/12.2683128

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