ArticleFundamental research2026
Physics-driven deep learning photoacoustic tomography.
Article in Fundamental research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- A Model-Based Joint Feature Fusion framework for limited-view photoacoustic tomography reconstruction.Photoacoustics · 2026Article
- Multi-frame pixel intensity consistency-based artifact reduction for photoacoustic computed tomography.Biomedical optics express · 2026Article
- Advances in photoacoustic imaging reconstruction and quantitative analysis for biomedical applications.Visual computing for industry, biomedicine, and art · 2026Review
- A Physics-Informed Neural Network forResearch square · 2026Article
- Learning a Filtered Backprojection Reconstruction Method for Photoacoustic Computed Tomography With Hemispherical Measurement Geometries.IEEE transactions on medical imaging · 2026Article
- Artifacts in photoacoustic imaging: Origins and mitigations.Photoacoustics · 2025Article
- Sinogram to image: direct reconstruction of photoacoustic tomography image using a hybrid deep learning approach.Biomedical optics express · 2025Article
- Ultrafast filtered back-projection for photoacoustic computed tomography.Biomedical optics express · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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Abstract
Photoacoustic tomography (PAT) is a rapidly emerging biomedical imaging modality. To achieve high-performance imaging, signal acquisition in PAT should ideally meet conditions such as full-view detection and dense sampling. However, these circumstances are rarely met in reality, leading to the ill-posed problem of image reconstruction from incomplete projections. Here we propose a physics-driven deep learning-based filtered back projection (dFBP) framework to address this important challenge. The dFBP network is inspired and constructed based on the physical model of the analytical filtered back projection algorithm and consists of a filtering module, a back-projection module, and a fusion module connected in cascade. The dFBP network is driven by physics and is thus interpretable and easy to train while being highly robust and versatile. Numerical and experimental validation on animals and humans show that dFBP-based PAT can achieve direct signal-to-image transformation with enhanced accuracy and reconstruct high-quality, artifact-suppressed images from sparse-view, limited-view, and acoustic heterogeneity-contaminated projections. The proposed dFBP provides a practical solution for high-performance PAT imaging under non-ideal conditions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.