Forward and adjoint radiance Monte Carlo models for quantitative photoacoustic imaging

Roman Hochuli; Samuel Powell; Simon R. Arridge; Ben Cox

doi:10.1117/12.2081407

11 March 2015 Forward and adjoint radiance Monte Carlo models for quantitative photoacoustic imaging

Roman Hochuli, Samuel Powell, Simon R. Arridge, Ben Cox

Proceedings Volume 9323, Photons Plus Ultrasound: Imaging and Sensing 2015; 93231P (2015) https://doi.org/10.1117/12.2081407
Event: SPIE BiOS, 2015, San Francisco, California, United States

Abstract

In quantitative photoacoustic imaging, the aim is to recover physiologically relevant tissue parameters such as chromophore concentrations or oxygen saturation. Obtaining accurate estimates is challenging due to the non-linear relationship between the concentrations and the photoacoustic images. Nonlinear least squares inversions designed to tackle this problem require a model of light transport, the most accurate of which is the radiative transfer equation. This paper presents a highly scalable Monte Carlo model of light transport that computes the radiance in 2D using a Fourier basis to discretise in angle. The model was validated against a 2D finite element model of the radiative transfer equation, and was used to compute gradients of an error functional with respect to the absorption and scattering coefficient. It was found that adjoint-based gradient calculations were much more robust to inherent Monte Carlo noise than a finite difference approach. Furthermore, the Fourier angular discretisation allowed very efficient gradient calculations as sums of Fourier coefficients. These advantages, along with the high parallelisability of Monte Carlo models, makes this approach an attractive candidate as a light model for quantitative inversion in photoacoustic imaging.

Citation Download Citation

Roman Hochuli, Samuel Powell, Simon R. Arridge, and Ben Cox "Forward and adjoint radiance Monte Carlo models for quantitative photoacoustic imaging", Proc. SPIE 9323, Photons Plus Ultrasound: Imaging and Sensing 2015, 93231P (11 March 2015); https://doi.org/10.1117/12.2081407

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