Combined Monte Carlo and k-Wave Simulations for Reconstruction of Blood Oxygen Saturation in Optoacoustics: A Pilot Study

Abstract

Optoacoustic (OA) imaging of biological tissues is a modern technique allowing for three-dimensional blood oxygen saturation mapping based on OA spectroscopy data. Since biological tissues are optically inhomogeneous and the spatial distribution of optical parameters within a biological tissue is a priori unknown, Monte Carlo simulation technique is traditionally used to estimate the distribution of probing illumination within tissues in quantitative OA reconstruction. Currently, machine learning techniques are actively employed for reconstructing 3D distribution of blood oxygen saturation or estimating optical properties of biological tissues based on training datasets. In this paper, systemic calculations of synthetic OA images of a medium with embedded vessel-like structures were performed to create a training dataset for machine learning employing combined application of the Monte Carlo technique for direct solution of optical problem and difference-space pseudo-spectral approach implemented through k-Wave Toolbox calculations for the acoustical part. The calculations were performed for probing wavelengths of 532 nm, 658 nm and 1064 nm, which are commonly employed in spectral OA imaging. Simulated OA data for different orientation, diameter and embedding depth of blood vessels allows analyzing the effect of these parameters on the formation of OA image and the reconstruction of blood oxygen saturation. The ratio of OA signals corresponding to probing wavelengths of 658 nm and 1064 nm was employed for simple reconstruction of blood oxygen saturation in silico for different vessel geometries with the precision of < 3–15% for the most of blood vessels diameters and embedding depths and the range of blood oxygen saturation values ≥ 0.8. The obtained set of synthetic OA data has high potential as a training set for employment in machine learning techniques aiming at mapping blood oxygenation based on spectral OA data.