Abstract
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.
Abstract (translated)
电气阻抗断层成像（EIT）是一种通过电边界测量来恢复物理体内导电性的非侵入性成像方法。结合机器学习，EIT在中风分类方面显示出巨大潜力。然而，大多数以前的工作都使用原始的EIT电压数据作为网络输入。我们基于最近的一项研究发展而来，该研究表明可以使用特殊的抗噪能力强的虚拟混合边缘检测（VHED）函数作为网络输入，尽管这项工作仅使用了高度简化的数学理想模型。 在这项工作中，我们将进一步论证利用EIT和VHED函数在中风分类中的应用。我们设计具有高细节度和数学现实性的模型来测试VHED函数作为输入的效果。通过一个包含已知会导致实际成像场景挑战的物理详细二维头部模型创建虚拟患者，并从统计上真实分布中抽取导电性值，将这些虚拟人设为患有不同形状和大小的出血或缺血性中风。 我们使用现实中的完整电极模型（CEM）而非数学理想的连续体模型来生成包含噪声的EIT电极数据，并通过该数据获取VHED函数。我们将作为输入使用的VHED函数的效果与使用原始EIT电压的替代方案进行了比较。 我们的结果显示，(i) 使用物理详细且数学现实性的二维EIT数据可以以高精度进行中风分类；(ii) 在存在噪声的情况下，VHED函数相较于原始数据能更好地作为网络输入。
URL
https://arxiv.org/abs/2501.14704
