Self-supervised light fluence correction network for photoacoustic tomography based on diffusion equation
Deep learning (DL) shows promise in estimating the absorption coefficient distribution of biological tissue in quantitative photoacoustic tomography (QPAT) imaging, but its application is limited by a lack of ground truth for supervised network training. To address this issue, we propose a DL-based...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-04-01
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| Series: | Photoacoustics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213597925000035 |
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| author | Zhaoyong Liang Zongxin Mo Shuangyang Zhang Long Chen Danni Wang Chaobin Hu Li Qi |
| author_facet | Zhaoyong Liang Zongxin Mo Shuangyang Zhang Long Chen Danni Wang Chaobin Hu Li Qi |
| author_sort | Zhaoyong Liang |
| collection | DOAJ |
| description | Deep learning (DL) shows promise in estimating the absorption coefficient distribution of biological tissue in quantitative photoacoustic tomography (QPAT) imaging, but its application is limited by a lack of ground truth for supervised network training. To address this issue, we propose a DL-based light fluence correction method that only uses the original PAT images for network training. Our self-supervised QPAT network model, which we termed SQPA-Net, introduces light fluence estimation based on diffusion equation to the loss function, and thus guides the model to learn an implicit representation of photoacoustic light transport within tissue. Simulation and small animal imaging experiments demonstrate the effectiveness and efficiency of our method. Compared to current DL-based methods and traditional iterative correction method, the proposed SQPA-Net achieves better light fluence correction results and significantly reduces the processing time. |
| format | Article |
| id | doaj-art-17f2aa9afd824e76b0717c41cd0ddcde |
| institution | OA Journals |
| issn | 2213-5979 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Photoacoustics |
| spelling | doaj-art-17f2aa9afd824e76b0717c41cd0ddcde2025-08-20T02:00:59ZengElsevierPhotoacoustics2213-59792025-04-014210068410.1016/j.pacs.2025.100684Self-supervised light fluence correction network for photoacoustic tomography based on diffusion equationZhaoyong Liang0Zongxin Mo1Shuangyang Zhang2Long Chen3Danni Wang4Chaobin Hu5Li Qi6School of Biomedical Engineering, Southern Medical University, 1023 Shatai Rd., Baiyun District, Guangzhou, Guangdong 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Rd., Baiyun District, Guangzhou, Guangdong 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Rd., Baiyun District, Guangzhou, Guangdong 510515, ChinaSchool of Biomedical Engineering, Southern Medical University, 1023 Shatai Rd., Baiyun District, Guangzhou, Guangdong 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Rd., Baiyun District, Guangzhou, Guangdong 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Rd., Baiyun District, Guangzhou, Guangdong 510515, ChinaSchool of Biomedical Engineering, Southern Medical University, 1023 Shatai Rd., Baiyun District, Guangzhou, Guangdong 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Rd., Baiyun District, Guangzhou, Guangdong 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Rd., Baiyun District, Guangzhou, Guangdong 510515, ChinaSchool of Biomedical Engineering, Southern Medical University, 1023 Shatai Rd., Baiyun District, Guangzhou, Guangdong 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Rd., Baiyun District, Guangzhou, Guangdong 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Rd., Baiyun District, Guangzhou, Guangdong 510515, ChinaSchool of Biomedical Engineering, Southern Medical University, 1023 Shatai Rd., Baiyun District, Guangzhou, Guangdong 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Rd., Baiyun District, Guangzhou, Guangdong 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Rd., Baiyun District, Guangzhou, Guangdong 510515, ChinaSchool of Biomedical Engineering, Southern Medical University, 1023 Shatai Rd., Baiyun District, Guangzhou, Guangdong 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Rd., Baiyun District, Guangzhou, Guangdong 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Rd., Baiyun District, Guangzhou, Guangdong 510515, ChinaSchool of Biomedical Engineering, Southern Medical University, 1023 Shatai Rd., Baiyun District, Guangzhou, Guangdong 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Rd., Baiyun District, Guangzhou, Guangdong 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, 1023 Shatai Rd., Baiyun District, Guangzhou, Guangdong 510515, China; Corresponding author at: School of Biomedical Engineering, Southern Medical University, 1023 Shatai Rd., Baiyun District, Guangzhou, Guangdong 510515, China.Deep learning (DL) shows promise in estimating the absorption coefficient distribution of biological tissue in quantitative photoacoustic tomography (QPAT) imaging, but its application is limited by a lack of ground truth for supervised network training. To address this issue, we propose a DL-based light fluence correction method that only uses the original PAT images for network training. Our self-supervised QPAT network model, which we termed SQPA-Net, introduces light fluence estimation based on diffusion equation to the loss function, and thus guides the model to learn an implicit representation of photoacoustic light transport within tissue. Simulation and small animal imaging experiments demonstrate the effectiveness and efficiency of our method. Compared to current DL-based methods and traditional iterative correction method, the proposed SQPA-Net achieves better light fluence correction results and significantly reduces the processing time.http://www.sciencedirect.com/science/article/pii/S2213597925000035Quantitative photoacoustic tomographyLight fluence correctionSelf-supervised learningAbsorption coefficient estimation |
| spellingShingle | Zhaoyong Liang Zongxin Mo Shuangyang Zhang Long Chen Danni Wang Chaobin Hu Li Qi Self-supervised light fluence correction network for photoacoustic tomography based on diffusion equation Photoacoustics Quantitative photoacoustic tomography Light fluence correction Self-supervised learning Absorption coefficient estimation |
| title | Self-supervised light fluence correction network for photoacoustic tomography based on diffusion equation |
| title_full | Self-supervised light fluence correction network for photoacoustic tomography based on diffusion equation |
| title_fullStr | Self-supervised light fluence correction network for photoacoustic tomography based on diffusion equation |
| title_full_unstemmed | Self-supervised light fluence correction network for photoacoustic tomography based on diffusion equation |
| title_short | Self-supervised light fluence correction network for photoacoustic tomography based on diffusion equation |
| title_sort | self supervised light fluence correction network for photoacoustic tomography based on diffusion equation |
| topic | Quantitative photoacoustic tomography Light fluence correction Self-supervised learning Absorption coefficient estimation |
| url | http://www.sciencedirect.com/science/article/pii/S2213597925000035 |
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