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A novel post-processing scheme for two-dimensional electrical impedance tomography based on artificial neural networks

机译:基于人工神经网络的二维电阻抗层析成像后处理方案

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摘要

ObjectiveElectrical Impedance Tomography (EIT) is a powerful non-invasive technique for imaging applications. The goal is to estimate the electrical properties of living tissues by measuring the potential at the boundary of the domain. Being safe with respect to patient health, non-invasive, and having no known hazards, EIT is an attractive and promising technology. However, it suffers from a particular technical difficulty, which consists of solving a nonlinear inverse problem in real time. Several nonlinear approaches have been proposed as a replacement for the linear solver, but in practice very few are capable of stable, high-quality, and real-time EIT imaging because of their very low robustness to errors and inaccurate modeling, or because they require considerable computational effort.
机译:客观电阻抗层析成像(EIT)是一种功能强大的非侵入性成像技术。目的是通过测量畴边界处的电势来估计活体组织的电特性。 EIT在患者健康方面是安全的,无创的,并且没有已知的危害,EIT是一种有吸引力的技术。但是,它具有特定的技术难度,其中包括实时解决非线性逆问题。有人提出了几种非线性方法来代替线性求解器,但实际上,由于对误差和建模不准确的鲁棒性很低,或者由于它们要求的稳定性,很少能进行稳定,高质量和实时的EIT成像。大量的计算工作。

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