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- W4384697274 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." @default.
- W4384697274 created "2023-07-20" @default.
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- W4384697274 date "2023-07-18" @default.
- W4384697274 modified "2023-09-23" @default.
- W4384697274 title "Artifact removal with physical model and deep learning for limited-data photoacoustic tomography" @default.
- W4384697274 doi "https://doi.org/10.1117/12.2683127" @default.
- W4384697274 hasPublicationYear "2023" @default.
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