Center for Mathematical Analysis and Computation, Yonsei University, Seoul 03722, Republic of Korea.
National Institute for Mathematical Science, Daejeon 34047, Republic of Korea.
Comput Math Methods Med. 2020 Jun 11;2020:9657372. doi: 10.1155/2020/9657372. eCollection 2020.
This paper proposes a deep learning method based on electrical impedance tomography (EIT) to estimate the thickness of abdominal subcutaneous fat. EIT for evaluating the thickness of abdominal subcutaneous fat is an absolute imaging problem that aims at reconstructing conductivity distributions from current-to-voltage data. Existing reconstruction methods based on EIT have difficulty handling the inherent drawbacks of strong nonlinearity and severe ill-posedness of EIT; hence, absolute imaging may not be possible using linearized methods. To handle nonlinearity and ill-posedness, we propose a deep learning method that finds useful solutions within a restricted admissible set by accounting for prior information regarding abdominal anatomy. We determined that a specially designed training dataset used during the deep learning process significantly reduces ill-posedness in the absolute EIT problem. In the preprocessing stage, we normalize current-voltage data to alleviate the effects of electrodeposition and body geometry by exploiting knowledge regarding electrode positions and body geometry. The performance of the proposed method is demonstrated through numerical simulations and phantom experiments using a 10 channel EIT system and a human-like domain.
本文提出了一种基于电阻抗断层成像（EIT）的深度学习方法，用于估计腹部皮下脂肪的厚度。EIT 用于评估腹部皮下脂肪的厚度是一个绝对成像问题，旨在从电流-电压数据重建电导率分布。现有的基于 EIT 的重建方法在处理 EIT 固有的强非线性和严重不适定性方面存在困难；因此，线性化方法可能无法实现绝对成像。为了处理非线性和不适定性，我们提出了一种深度学习方法，该方法通过考虑有关腹部解剖结构的先验信息，在受限的可接受集合内找到有用的解决方案。我们确定，在深度学习过程中使用的专门设计的训练数据集可显著降低绝对 EIT 问题中的不适定性。在预处理阶段，我们通过利用电极位置和人体几何形状的知识，对电流-电压数据进行归一化，以减轻电沉积和身体几何形状的影响。使用 10 通道 EIT 系统和类人域进行数值模拟和体模实验，验证了所提出方法的性能。
