Electrical impedance tomography (EIT) is a non-invasive imaging method for recovering the internal conductivity of a physical body from electric boundary measurements. EIT combined with machine learning has shown promise for the classification of strokes. However, most previous works have used raw EIT voltage data as network inputs. We build upon a recent development which suggested the use of special noise-robust Virtual Hybrid Edge Detection (VHED) functions as network inputs, although that work used only highly simplified and mathematically ideal models. In this work we strengthen the case for the use of EIT, and VHED functions especially, for stroke classification. We design models with high detail and mathematical realism to test the use of VHED functions as inputs. Virtual patients are created using a physically detailed 2D head model which includes features known to create challenges in real-world imaging scenarios. Conductivity values are drawn from statistically realistic distributions, and phantoms are afflicted with either hemorrhagic or ischemic strokes of various shapes and sizes. Simulated noisy EIT electrode data, generated using the realistic Complete Electrode Model (CEM) as opposed to the mathematically ideal continuum model, is processed to obtain VHED functions. We compare the use of VHED functions as inputs against the alternative paradigm of using raw EIT voltages. Our results show that (i) stroke classification can be performed with high accuracy using 2D EIT data from physically detailed and mathematically realistic models, and (ii) in the presence of noise, VHED functions outperform raw data as network inputs.

翻译：电阻抗断层成像（EIT）是一种通过边界电学测量重建物体内部电导率的无创成像方法。结合机器学习的EIT技术在脑卒中分类中展现出潜力。然而，现有研究大多直接使用原始EIT电压数据作为网络输入。本研究基于近期提出的采用抗噪型虚拟混合边缘检测（VHED）函数作为网络输入的方案进行拓展——该方案此前仅使用高度简化的理想数学模型。本文通过构建具有高细节度与数学真实性的模型，系统验证VHED函数在脑卒中分类中的输入有效性。我们采用包含真实成像挑战特征的精细化二维头部模型生成虚拟患者，其电导率取值遵循统计学真实分布，并模拟了不同形态与尺寸的出血性或缺血性脑卒中病灶。通过采用具有物理真实性的完整电极模型（而非理想连续介质模型）生成含噪声的EIT电极仿真数据，进而处理得到VHED函数。通过对比VHED函数与原始EIT电压两种输入范式，研究发现：（1）基于精细化物理模型与数学真实模型生成的二维EIT数据可实现高精度脑卒中分类；（2）在噪声存在条件下，VHED函数作为网络输入的性能显著优于原始数据。