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arXiv · 2011.12520

Deep Learning of Diffuse Optical Tomography based on Time-Domain Radiative Transfer Equation

Yu-ichi Takamizu·Masayuki Umemura·Hidenobu Yajima·Makito Abe·Yoko Hoshi
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Abstract

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.

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Yu-ichi Takamizu, Masayuki Umemura, Hidenobu Yajima, Makito Abe, Yoko Hoshi. 2020-11-25. Deep Learning of Diffuse Optical Tomography based on Time-Domain Radiative Transfer Equation. https://arxiv.org/abs/2011.12520

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