Hänninen, N;
Pulkkinen, A;
Arridge, S;
Tarvainen, T;
(2022)
Adaptive stochastic Gauss-Newton method with optical Monte Carlo for quantitative photoacoustic tomography.
Journal of Biomedical Optics
, 27
(8)
, Article 083013. 10.1117/1.JBO.27.8.083013.
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Abstract
SIGNIFICANCE: The 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. AIM: The 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. RESULTS: The 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. CONCLUSIONS: The A-SGN method with a norm test can be utilized in QPAT to provide accurate and computationally efficient solutions.
| Type: | Article |
|---|---|
| Title: | Adaptive stochastic Gauss-Newton method with optical Monte Carlo for quantitative photoacoustic tomography |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1117/1.JBO.27.8.083013 |
| Publisher version: | https://doi.org/10.1117/1.JBO.27.8.083013 |
| Language: | English |
| Additional information: | © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. |
| Keywords: | Monte Carlo, inverse problems, quantitative photoacoustic tomography, stochastic optimization, Algorithms, Image Processing, Computer-Assisted, Monte Carlo Method, Photons, Tomography, X-Ray Computed |
| UCL classification: | UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10147152 |
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