Photoacoustic tomography (PAT) is a promising hybrid imaging technique with clinical potential, but it faces challenges due to limited-view reconstruction. This research develops a deep learning-based approach using a multi-view imaging system and a Uformer network to reconstruct high-resolution images from limited-angle input data. The results show state-of-the-art performance compared to conventional restoration models, highlighting the potential of this method for improving PAT in clinical settings. This novel strategy helps overcome limited-data challenges and contributes to the development of innovative imaging solutions for clinical applications.
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