ArticlePhotoacoustics2025
Self-supervised light fluence correction network for photoacoustic tomography based on diffusion equation.
Article in Photoacoustics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Geometry-dependent reachability, homogenization, and redundancy in multi-view photoacoustic illumination.Biomedical optics express · 2026Article
- Application of a virtual imaging framework for investigating a deep learning-based reconstruction method for 3D quantitative photoacoustic computed tomography.Photoacoustics · 2026Article
- Towards bridging the synthetic-to-real gap in quantitative photoacoustic tomography via unsupervised domain adaptation.Photoacoustics · 2025Article
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7 authors.
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Abstract
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.
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