脑室内阻抗成像的神经网络重建算法

G. W. Walker, S. Kun, R. Peura
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引用次数: 0

摘要

目前正在开发一种心室阻抗成像(III)系统,该系统将用于通过心室导管评估心脏的电和机械特性。需要解决的主要问题之一是心室内导管位置的确定。现有的确定心室内导管位置的方法,包括x射线、透视和计算机断层扫描,虽然准确,但繁琐、昂贵,并且无法确定连续的实时心室内导管位置。这项工作的目的是开发一种基于人工神经网络(ANN)的重建算法,该算法将用于处理来自导管的电信息,以确定连续的、实时的脑室内导管位置。利用计算机模拟III系统的结果训练了一个反向传播神经网络。神经网络对期望输出变量的预测误差在0.05% ~ 1.8%之间,相关系数(r)在83% ~ 99%之间,输出变量的均方根误差为4.9%,这表明神经网络在确定连续实时脑室导管位置方面具有很大的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A neural network reconstruction algorithm for Intraventricular Impedance Imaging
An Intraventricular Impedance Imaging (III) system, that will be used for assessing electrical and mechanical cardiac properties via an intraventricular catheter, is presently under development. One of the major problems to be solved is the determination of the intraventricular catheter position within the ventricle. Existing methods for determining catheter position within a cardiac ventricle, including X-ray, fluoroscopy and computer tomography, are accurate but cumbersome, expensive, and unable to ascertain the continuous real-time intraventricular catheter position. The purpose of this work was to develop a reconstruction algorithm, based on Artificial Neural Networks (ANN), which will be used to process the electrical information from the catheter to ascertain the continuous, real-time intraventricular catheter position. A back-propagation neural network was trained using results from computer simulations of the III system. The neural network predicted the desired output variables with errors ranging from 0.05% to 1.8% and correlation coefficients (r) ranging from 83% to 99% The RMS error of the output variables was 4.9% These results indicate that ANN have great potential as a tool in determining the continuous real-time intraventricular catheter position.
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