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.

中文翻译：

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使用深度学习的基于电阻抗断层扫描的腹部皮下脂肪估计方法。


本文提出了一种基于电阻抗断层扫描（EIT）的深度学习方法来估计腹部皮下脂肪的厚度。用于评估腹部皮下脂肪厚度的 EIT 是一个绝对成像问题，旨在从电流到电压数据重建电导率分布。现有基于EIT的重构方法难以解决EIT非线性强、不适定性严重的固有缺陷；因此，使用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，通过考虑有关腹部解剖的先验信息，在有限的可接受范围内找到有用的解决方案。我们确定，在深度学习过程中使用专门设计的训练数据集可以显着减少绝对 EIT 问题的不适定性。在预处理阶段，我们通过利用有关电极位置和身体几何形状的知识，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。该方法的性能通过使用 10 通道 EIT 系统和类人域的数值模拟和模型实验得到了证明。