ArticlePhotoacoustics2025
Towards bridging the synthetic-to-real gap in quantitative photoacoustic tomography via unsupervised domain adaptation.
Article in Photoacoustics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- PIT-Net: Physics-informed transformer with differentiable diffusion constraints for quantitative photoacoustic tomography of deep tissues.Photoacoustics · 2026Article
- Geometry-dependent reachability, homogenization, and redundancy in multi-view photoacoustic illumination.Biomedical optics express · 2026Article
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Authors and funding
4 authors.
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Abstract
The difficulty of obtaining absorption coefficient annotations hinders the practical application of deep learning in quantitative photoacoustic tomography. While training on synthetic data is easy to implement, the synthetic-to-real domain gap poses a significant challenge to model generalization. To address this, we propose a Decoder-enhanced unsupervised Domain Adaptation (DDA) framework to enable knowledge transfer from synthetic data to an unlabeled target domain. Experimental results show that DDA significantly improves estimation performance on target images and surpasses competing methods in quantitative evaluation and visual comparison. Additionally, we investigate the effect of cross-domain label distribution similarity on domain adaptation and recommend an effective approach for data synthesis. To mitigate the effect of absorption property mismatch, we propose fine-tuning the affine parameters of normalization layers, which significantly improves estimation accuracy using labeled multi-wavelength photoacoustic images from as few as two target samples.
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Registered trials
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