ArticlePhotoacoustics2024
Deep learning acceleration of iterative model-based light fluence correction for photoacoustic tomography.
Article in Photoacoustics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 9 citations in OpenAlex.
- Volumetric photoacoustic imaging of elastin and age-related remodeling using near-infrared probe ElaNIR.Photoacoustics · 2026Article
- Geometry-dependent reachability, homogenization, and redundancy in multi-view photoacoustic illumination.Biomedical optics express · 2026Article
- Advances in photoacoustic imaging reconstruction and quantitative analysis for biomedical applications.Visual computing for industry, biomedicine, and art · 2026Review
- Depth-dependent fluence compensation withoutJournal of biomedical optics · 2025Article
- A clinical feasibility study of a photoacoustic finder for sentinel lymph node biopsy in breast cancer patients: A prospective cross-sectional study.Photoacoustics · 2025Article
- Self-supervised light fluence correction network for photoacoustic tomography based on diffusion equation.Photoacoustics · 2025Article
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Photoacoustic tomography (PAT) is a promising imaging technique that can visualize the distribution of chromophores within biological tissue. However, the accuracy of PAT imaging is compromised by light fluence (LF), which hinders the quantification of light absorbers. Currently, model-based iterative methods are used for LF correction, but they require extensive computational resources due to repeated LF estimation based on differential light transport models. To improve LF correction efficiency, we propose to use Fourier neural operator (FNO), a neural network specially designed for estimating partial differential equations, to learn the forward projection of light transport in PAT. Trained using paired finite-element-based LF simulation data, our FNO model replaces the traditional computational heavy LF estimator during iterative correction, such that the correction procedure is considerably accelerated. Simulation and experimental results demonstrate that our method achieves comparable LF correction quality to traditional iterative methods while reducing the correction time by over 30 times.
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