Electrical impedance tomography (EIT) uses non-invasive and non-radiative imaging to detect inhomogeneous electrical properties in tissues. The inverse problem of EIT is a highly nonlinear, ill-posed problem, which causes inaccuracy in target size calculation. We propose a novel approach to discretize the target size and use a neural network (NN) classifier to classify the unknown size in discrete steps. The target size is discretized into distinct steps, and each step can be a unique class. The data is pre-processed with the cumulative distribution transform (CDT) to enhance distinguishability. First, the NN is trained with simulated datasets, divided into time difference (t-d) group and CDT group. After training, the NN classifier is tested by experimental data recorded in a phantom experiment. Linear discriminant analysis (LDA) is performed to assess the distinguishability of classes. There is a significant increase in distance between classes after the CDT pre-processing. The density of the classes has an upward trend with a higher degree of clustering after CDT pre-processing. The CDT data clustering into distinguishable classes is essential to excellent NN classification results. Such an approach is a significant paradigm shift by turning the cumbersome inverse calculation with uncertain accuracy into a classification problem with predetermined step errors. The accuracy and resolution can be further extended by increasing the discretization steps.

中文翻译：

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使用神经网络分类器进行电阻抗断层扫描中的离散目标尺寸检测

电阻抗断层扫描 (EIT) 使用非侵入性和非辐射成像来检测组织中的不均匀电特性。EIT的逆问题是一个高度非线性的不适定问题，会导致目标尺寸计算不准确。我们提出了一种离散化目标大小的新方法，并使用神经网络 (NN) 分类器在离散步骤中对未知大小进行分类。目标大小被离散为不同的步骤，每个步骤都可以是一个唯一的类。使用累积分布变换 (CDT) 对数据进行预处理以增强可区分性。首先，神经网络用模拟数据集进行训练，分为时间差 (td) 组和 CDT 组。训练后，NN 分类器通过在幻像实验中记录的实验数据进行测试。执行线性判别分析 (LDA) 以评估类别的可区分性。CDT 预处理后，类之间的距离显着增加。CDT预处理后，类的密度有上升趋势，聚类程度更高。将 CDT 数据聚类为可区分的类别对于出色的 NN 分类结果至关重要。这种方法是一种重大的范式转变，将具有不确定精度的繁琐逆计算转化为具有预定步长误差的分类问题。通过增加离散化步骤可以进一步提高精度和分辨率。CDT预处理后，类的密度有上升趋势，聚类程度更高。将 CDT 数据聚类为可区分的类别对于出色的 NN 分类结果至关重要。这种方法是一种重大的范式转变，将具有不确定精度的繁琐逆计算转化为具有预定步长误差的分类问题。通过增加离散化步骤可以进一步提高精度和分辨率。CDT预处理后，类的密度有上升趋势，聚类程度更高。将 CDT 数据聚类为可区分的类别对于出色的 NN 分类结果至关重要。这种方法是一种重大的范式转变，将具有不确定精度的繁琐逆计算转化为具有预定步长误差的分类问题。通过增加离散化步骤可以进一步提高精度和分辨率。