Publications by authors named "Shangkun Hou"
Article Synopsis
- Photoacoustic tomography (PAT) is a cutting-edge imaging technique that creates high-resolution images of biological tissues, but often struggles with artifact issues in limited-view scenarios.
- This study introduces a new reconstruction strategy that uses multiple diffusion models and alternating iteration methods to fill in missing data, which leads to improved image quality and stability.
- When tested on a dataset with only 60° views, the method showed a significant increase in image clarity with improvements in peak signal-to-noise ratio and structural similarity, indicating a promising advancement for clinical applications of PAT.
Traditional methods under sparse view for reconstruction of photoacoustic tomography (PAT) often result in significant artifacts. Here, a novel image to image transformation method based on unsupervised learning artifact disentanglement network (ADN), named PAT-ADN, was proposed to address the issue. This network is equipped with specialized encoders and decoders that are responsible for encoding and decoding the artifacts and content components of unpaired images, respectively.
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