Adaptive stochastic Gauss-Newton method with optical Monte Carlo for quantitative photoacoustic tomography
Niko Hänninen1, Aki Pulkkinen1, Simon Arridge2
1University of Eastern Finland, Department of Applied Physics, Kuopio, Finland.
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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