Machine-learning-enhanced image reconstruction in optical tomography using the Monte Carlo method for light transport

Source: PubMed Central Open Access, NCBI / U.S. National Library of Medicine

Abstract. Significance The Monte Carlo method for light transport is widely accepted as an accurate method for simulating light propagation in a scattering medium. Its use in optical tomography, however, suffers from inherent stochastic noise. This noise is present in both evaluations of the forward model, as well as in the search direction of the minimization algorithm used for image reconstruction. Aim We aim to utilize machine learning to compensate for the stochastic Monte Carlo noise in the reconstruction of absorption and scattering in optical tomography. Approach An iterative image reconstruction algorithm is proposed. The algorithm uses convolutional neural networks in a stochastic Gauss–Newton update when estimating absorption and scattering coefficients. Results The methodology is evaluated using numerical simulations and compared against the conventional stochastic Gauss–Newton algorithm in optical tomography. It is demonstrated that the methodology can be used to compensate for image reconstruction artifacts caused by the stochastic noise. Conclusions The proposed machine learning approach can be used to compensate for stochastic noise in Gauss–Newton iterations, and it enables reconstruction of absorption and scattering with a significantly lower number of photons than a conventional stochastic Gauss–Newton algorithm.