Artifact removal with physical model and deep learning for limited-data photoacoustic tomography

Wenhua Zhong; Guijun Wang; Hongyu Zhang; Xiaoling Xu; Qiegen Liu; Xianlin Song

doi:10.1117/12.2683127

18 July 2023 Artifact removal with physical model and deep learning for limited-data photoacoustic tomography

Wenhua Zhong, Guijun Wang, Hongyu Zhang, Xiaoling Xu, Qiegen Liu, Xianlin Song

Proceedings Volume 12745, Sixteenth International Conference on Photonics and Imaging in Biology and Medicine (PIBM 2023); 127450I (2023) https://doi.org/10.1117/12.2683127
Event: Sixteenth International Conference on Photonics and Imaging in Biology and Medicine (PIBM 2023), 2023, Haikou, China

Abstract

Artifact in photoacoustic tomography is always an issue to be solved. Here, a deep learning based physical model method to remove artifact for limited-data photoacoustic tomography was proposed, termed as PD net. A virtual photoacoustic tomography platform was constructed based on k-Wave, and the dataset required for deep learning was obtained using this virtual platform. The U-Net was used to build a deep learning network to remove artifacts in sparse-view and limited-view photoacoustic tomography. Under sparsity condition, when the number of ultrasonic transducers is 64, the improvement rates of SSIM and PSNR of the network are 274% and 66.34%, respectively, compared with the input of the network, which verifies that this method can remove artifacts in sparse-view photoacoustic tomography. The proposed method can reduce artifacts and enhance anatomical contrast when the number of ultrasonic transducers used is limited, and effectively reduce manufacturing costs of photoacoustic tomography.

Citation Download Citation

Wenhua Zhong, Guijun Wang, Hongyu Zhang, Xiaoling Xu, Qiegen Liu, and Xianlin Song "Artifact removal with physical model and deep learning for limited-data photoacoustic tomography", Proc. SPIE 12745, Sixteenth International Conference on Photonics and Imaging in Biology and Medicine (PIBM 2023), 127450I (18 July 2023); https://doi.org/10.1117/12.2683127

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