基于自适应网格分组方法的EIT快速静态图像重建

K. Cho, E. Woo, Sung Tack Ko
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引用次数: 2

摘要

对于电阻抗层析成像技术的实际应用来说,在合理的处理时间内重建具有较高空间分辨率的静态图像是至关重要的。然而,传统的EIT静态图像重建算法在试图获得更高的空间分辨率时,处理时间迅速增加,收敛性差。为了克服这一问题,提出了一种基于模糊遗传算法的自适应网格分组方法。将改进的Newton-Raphson方法与自适应网格分组算法相结合的计算机模拟结果表明,在不牺牲空间分辨率的情况下,可以显著减少计算时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fast Static Image Reconstruction using Adaptive Mesh Grouping Method in EIT
For the practical applications of the electrical impedance tomography (EIT) technology, it is essential to reconstruct static images with a higher spatial resolution in a reasonable amount of processing time. Using the conventional EIT static image reconstruction algorithms, however, the processing time increases rapidly with poor convergence characteristics as we try to get a higher spatial resolution. In order to overcome this problem, we propose an adaptive mesh grouping method based on a fuzzy-GA algorithm. Computer simulations using the improved Newton-Raphson method combined with the adaptive mesh grouping algorithm showed a promising result that we can significantly reduce the computation time without sacrificing the spatial resolution.
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