Image reconstruction in quantitative photoacoustic tomography using adaptive optical Monte Carlo

Niko Hänninen; Aki Pulkkinen; Simon Arridge; Tanja Tarvainen

doi:10.1117/12.2647252

9 March 2023 Image reconstruction in quantitative photoacoustic tomography using adaptive optical Monte Carlo

Niko Hänninen, Aki Pulkkinen, Simon Arridge, Tanja Tarvainen

Proceedings Volume 12379, Photons Plus Ultrasound: Imaging and Sensing 2023; 1237916 (2023) https://doi.org/10.1117/12.2647252
Event: SPIE BiOS, 2023, San Francisco, California, United States

Conference Poster

Abstract

In quantitative photoacoustic tomography (QPAT), distributions of optical parameters inside the target are reconstructed from photoacoustic images. In this work, we utilize the Monte Carlo (MC) method for light transport in the image reconstruction of QPAT. Modeling light transport accurately with the MC requires simulating a large number of photon packets, which can be computationally expensive. On the other hand, too low number of photon packets results in a high level of stochastic noise, which can lead to significant errors in reconstructed images. In this work, we use an adaptive approach, where the number of simulated photon packets is adjusted during an iterative image reconstruction. It is based on a norm test where the expected relative error of the minimization direction is controlled. The adaptive approach automatically determines the number of simulated photon packets to provide sufficiently accurate light transport modeling without unnecessary computational burden. The presented approach is studied with two-dimensional simulations.

Citation Download Citation

Niko Hänninen, Aki Pulkkinen, Simon Arridge, and Tanja Tarvainen "Image reconstruction in quantitative photoacoustic tomography using adaptive optical Monte Carlo", Proc. SPIE 12379, Photons Plus Ultrasound: Imaging and Sensing 2023, 1237916 (9 March 2023); https://doi.org/10.1117/12.2647252

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