| Autor: |
Wenhua Zhong, Tianle Li, Shangkun Hou, Hongyu Zhang, Zilong Li, Guijun Wang, Qiegen Liu, Xianlin Song |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Photoacoustics, Vol 38, Iss , Pp 100613- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2213-5979 |
| DOI: |
10.1016/j.pacs.2024.100613 |
| Popis: |
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. The performance of the proposed PAT-ADN was evaluated using circular phantom data and the animal in vivo experimental data. The results demonstrate that PAT-ADN exhibits excellent performance in effectively removing artifacts. In particular, under extremely sparse view (e.g., 16 projections), structural similarity index and peak signal-to-noise ratio are improved by ∼188 % and ∼85 % in in vivo experimental data using the proposed method compared to traditional reconstruction methods. PAT-ADN improves the imaging performance of PAT, opening up possibilities for its application in multiple domains. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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