Diffuse optical tomography (DOT) has been investigated as an alternative imaging modality for breast cancer detection thanks to its excellent contrast to hemoglobin oxidization level. However, due to the complicated non-linear photon scattering physics and ill-posedness, the conventional reconstruction algorithms are sensitive to imaging parameters such as boundary conditions. To address this, here we propose a novel deep learning approach that learns non-linear photon scattering physics and obtains an accurate three dimensional (3D) distribution of optical anomalies. In contrast to the traditional black-box deep learning approaches, our deep network is designed to invert the Lippman-Schwinger integral equation using the recent mathematical theory of deep convolutional framelets. As an example of clinical relevance, we applied the method to our prototype DOT system. We show that our deep neural network, trained with only simulation data, can accurately recover the location of anomalies within biomimetic phantoms and live animals without the use of an exogenous contrast agent.

中文翻译：

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深度学习漫射光学层析成像。

漫射光学层析成像（DOT）由于与血红蛋白氧化水平具有出色的对比，已被研究作为乳腺癌检测的替代成像方式。然而，由于复杂的非线性光子散射物理学和不适定性，常规的重建算法对诸如边界条件的成像参数敏感。为了解决这个问题，我们在这里提出了一种新颖的深度学习方法，该方法可以学习非线性光子散射物理学并获得光学异常的精确三维（3D）分布。与传统的黑盒深度学习方法相比，我们的深度网络旨在使用最新的深度卷积框架理论来反转Lippman-Schwinger积分方程。作为临床相关性的一个例子，我们将该方法应用于我们的原型DOT系统。我们表明，仅使用模拟数据训练的深度神经网络无需使用外源性造影剂，就可以准确地恢复仿生幻影和活体动物中异常的位置。