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Publication Detail
Adaptive stochastic Gauss-Newton method with optical Monte Carlo for quantitative photoacoustic tomography
  • Publication Type:
    Journal article
  • Authors:
    Hänninen N, Pulkkinen A, Arridge S, Tarvainen T
  • Publisher:
    SPIE-Intl Soc Optical Eng
  • Publication date:
  • Journal:
    Journal of Biomedical Optics
  • Volume:
  • Issue:
  • Article number:
  • Medium:
  • Status:
  • Country:
    United States
  • Print ISSN:
  • PII:
  • Language:
  • Keywords:
    Monte Carlo, inverse problems, quantitative photoacoustic tomography, stochastic optimization, Algorithms, Image Processing, Computer-Assisted, Monte Carlo Method, Photons, Tomography, X-Ray Computed
  • Notes:
    © 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.
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.
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