Journal
PHOTOACOUSTICS
Volume 20, Issue -, Pages -Publisher
ELSEVIER GMBH
DOI: 10.1016/j.pacs.2020.100197
Keywords
Photoacoustic imaging; Deep learning; Image reconstruction
Categories
Funding
- Natural Science Foundation of Shanghai [18ZR1425000]
- National Natural Science Foundation of China [61805139]
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Conventional reconstruction algorithms (e.g., delay-and-sum) used in photoacoustic imaging (PAI) provide a fast solution while many artifacts remain, especially for limited-view with ill-posed problem. In this paper, we propose a new convolutional neural network (CNN) framework Y-Net: a CNN architecture to reconstruct the initial PA pressure distribution by optimizing both raw data and beamformed images once. The network combines two encoders with one decoder path, which optimally utilizes more information from raw data and beamformed image. We compared our result with some ablation studies, and the results of the test set show better performance compared with conventional reconstruction algorithms and other deep learning method (U-Net). Both in-vitro and in-vivo experiments are used to validated our method, which still performs better than other existing methods. The proposed Y-Net architecture also has high potential in medical image reconstruction for other imaging modalities beyond PAI.
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