Publication Date:
2018-02-12
Description:
Entropy, Vol. 20, Pages 121: Stochastic Proximal Gradient Algorithms for Multi-Source Quantitative Photoacoustic Tomography Entropy doi: 10.3390/e20020121 Authors: Simon Rabanser Lukas Neumann Markus Haltmeier The development of accurate and efficient image reconstruction algorithms is a central aspect of quantitative photoacoustic tomography (QPAT). In this paper, we address this issues for multi-source QPAT using the radiative transfer equation (RTE) as accurate model for light transport. The tissue parameters are jointly reconstructed from the acoustical data measured for each of the applied sources. We develop stochastic proximal gradient methods for multi-source QPAT, which are more efficient than standard proximal gradient methods in which a single iterative update has complexity proportional to the number applies sources. Additionally, we introduce a completely new formulation of QPAT as multilinear (MULL) inverse problem which avoids explicitly solving the RTE. The MULL formulation of QPAT is again addressed with stochastic proximal gradient methods. Numerical results for both approaches are presented. Besides the introduction of stochastic proximal gradient algorithms to QPAT, we consider the new MULL formulation of QPAT as main contribution of this paper.
Electronic ISSN:
1099-4300
Topics:
Chemistry and Pharmacology
,
Physics
Permalink