A machine learning model with physical constraints (ML-PC) is introduced to perform diffuse optical tomography (DOT) reconstruction. DOT reconstruction is an ill-posed and under-determined problem, and its quality suffers by model mismatches, complex boundary conditions, tissue-probe contact, noise etc. Here, for the first time, we combine ultrasound-guided DOT with ML to facilitate DOT reconstruction. Our method has two key components: (i) a neural network based on auto-encoder is adopted for DOT reconstruction, and (ii) physical constraints are implemented to achieve accurate reconstruction. Both qualitative and quantitative results demonstrate that the accuracy of the proposed method surpasses that of existing models. In a phantom study, compared with the Born conjugate gradient descent (Born-CGD) reconstruction method, the ML-PC method decreases the mean percentage error of the reconstructed maximum absorption coefficient from 16.41% to 13.4% for high contrast phantoms and from 23.42% to 9.06% for low contrast phantoms, with improved depth distribution of the target absorption maps. In a clinical study, better contrast was obtained between malignant and benign breast lesions, with the ratio of the medians of the maximum absorption coefficient improved from 1.63 to 2.22.

中文翻译：

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具有物理约束的漫射光学断层扫描机器学习模型

引入了具有物理约束的机器学习模型 (ML-PC) 来执行漫反射光学断层扫描 (DOT) 重建。DOT 重建是一个不适定和欠定问题，其质量受到模型不匹配、复杂边界条件、组织-探针接触、噪声等的影响。在这里，我们首次将超声引导的 DOT 与 ML 相结合，以促进DOT 重建。我们的方法有两个关键组成部分：（i）采用基于自动编码器的神经网络进行 DOT 重建，以及（ii）实施物理约束以实现准确的重建。定性和定量结果都表明，所提出方法的准确性优于现有模型。在一项体模研究中，与 Born 共轭梯度下降（Born-CGD）重建方法相比，ML-PC 方法将高对比度模型的重建最大吸收系数的平均百分比误差从 16.41% 降低到 13.4%，低对比度模型从 23.42% 降低到 9.06%，并改善了目标吸收图的深度分布。在一项临床研究中，恶性和良性乳腺病变之间的对比度更好，最大吸收系数的中位数比值从 1.63 提高到 2.22。