ArticleJournal of biomedical optics2022
Adaptive stochastic Gauss-Newton method with optical Monte Carlo for quantitative photoacoustic tomography.
Article in Journal of biomedical optics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 11 citations in OpenAlex.
- Machine-learning-enhanced image reconstruction in optical tomography using the Monte Carlo method for light transport.Journal of biomedical optics · 2026Article
- Stochastic Gauss-Newton method for estimating absorption and scattering in optical tomography with the Monte Carlo method for light transport.Biomedical optics express · 2024Article
- Quantitative photoacoustic tomography: modeling and inverse problems.Journal of biomedical optics · 2024Review
- Special Section Guest Editorial: Introduction to the Special Section Celebrating 30 years of Open Source Monte Carlo Codes in Biomedical Optics.Journal of biomedical optics · 2022Article
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
significanceThe image reconstruction problem in quantitative photoacoustic tomography (QPAT) is an ill-posed inverse problem. Monte Carlo method for light transport can be utilized in solving this image reconstruction problem.
aimThe aim was to develop an adaptive image reconstruction method where the number of photon packets in Monte Carlo simulation is varied to achieve a sufficient accuracy with reduced computational burden. APPROACH: The image reconstruction problem was formulated as a minimization problem. An adaptive stochastic Gauss-Newton (A-SGN) method combined with Monte Carlo method for light transport was developed. In the algorithm, the number of photon packets used on Gauss-Newton (GN) iteration was varied utilizing a so-called norm test.
resultsThe approach was evaluated with numerical simulations. With the proposed approach, the number of photon packets needed for solving the inverse problem was significantly smaller than in a conventional approach where the number of photon packets was fixed for each GN iteration.
conclusionsThe A-SGN method with a norm test can be utilized in QPAT to provide accurate and computationally efficient solutions.
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Registered trials
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