Near infrared diffuse optical tomography (DOT) provides an imaging modality for the oxygenation of tissue. In this paper, we propose a novel machine learning algorithm based on time-domain radiative transfer equation. We use temporal profiles of absorption measure for a two-dimensional model of target tissue, which are calculated by solving time-domain radiative transfer equation. Applying a long-short-term memory (LSTM) deep learning method, we find that we can specify positions of cancer cells with high accuracy rates. We demonstrate that the present algorithm can also predict multiple or extended cancer cells.
@article{arxiv.2011.12520,
title = {Deep Learning of Diffuse Optical Tomography based on Time-Domain Radiative Transfer Equation},
author = {Yu-ichi Takamizu and Masayuki Umemura and Hidenobu Yajima and Makito Abe and Yoko Hoshi},
journal= {arXiv preprint arXiv:2011.12520},
year = {2020}
}