基于时域辐射转移方程的漫射光学层析深度学习
Deep Learning of Diffuse Optical Tomography based on Time-Domain Radiative Transfer Equation
摘要：
近红外漫射光学断层扫描（DOT）提供了一种组织氧合的成像模式。本文提出了一种基于时域辐射转移方程的机器学习算法。我们使用的时间轮廓的吸收测量的二维模型的目标组织，这是通过求解时域辐射转移方程计算。应用长短期记忆（LSTM）深度学习方法，我们发现我们可以以高准确率指定癌细胞的位置。我们证明，本算法也可以预测多个或扩展的癌细胞。
英文摘要：
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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