Use of a priori information in estimating tissue resistivities -: a simulation study
Use of a priori information in estimating tissue resistivities -: a simulation study
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DOI：
10.1088/0031-9155/43/12/015
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发表时间：
1998-12-01
影响因子：
3.5
通讯作者：
Eyüboglu, BM
中科院分区：
文献类型：
--
作者：
Baysal, U;Eyüboglu, BM
在求解生物电场正反问题时，需要准确估计活体组织的电阻率，以构建可靠的人体体积导体模型。利用电阻抗断层成像中常用的HT四电极阻抗测量技术，可以获得估算所需的数据。本文将具有区域电阻率、线性化误差和仪器噪声统计特性的先验几何信息引入到一种新的电阻率估计算法中，称为统计约束最小均方误差估计器(MiMSEE)，以提高估计精度。MiMSEE从使用高分辨率成像设备获得的图像中获取几何信息。这项研究是Eyuboglu等人早期工作的扩展，并从两个数值模式中获得了模拟测量，该模式包含背景区域上的五个和六个区域。此外，通过使用最多8个多电流电极对来重复估计，以便在将测量数量增加到96个的同时观察估计性能的效果。将结果与传统的单程算法中的最小二乘误差估计器(LSEE)进行了比较。结果表明，MiMSEE估计误差比LSEE估计误差小27倍，LSEE误差适用于小的、高对比度的区域，例如主动脉。在估计区域电阻率时，MiMSEE算法所需的计算时间是LSEE的25.8倍(对于五个区域的电阻率分布)和22.2倍(对于六个区域的电阻率分布)。这两种算法之间的计算时间差距随着区域数目的增加而减小。
Accurate estimation of tissue resistivities in vivo is needed to construct reliable human body volume conductor models in solving forward and inverse bioelectric field problems. The necessary data for the estimation can be obtained by using ht four-electrode impedance measurement technique, usually employed in electrical impedance tomography. In this study, a priori geometrical information with statistical properties of regional resistivities and linearization error as well as instrumentation noise has been incorporated into a new resistivity estimation algorithm which is called a statistically constrained minimum mean squares error estimator (MiMSEE) to improve estimation accuracy. MiMSEE intakes geometrical information from the image which is obtained by using a high-resolution imaging modality. This study is an extension of earlier work by Eyuboglu et al and obtains simulated measurements from two numerical models containing five and six regions on a background region. Also, estimations are repeated by suing up to eight multiple current electrode pairs, in order to observe the effect of estimation performance while increasing the number of measurements up to 96. The results are compared with a conventional least squares error estimator (LSEE) which is used in one-pass algorithms. It is shown that the MiMSEE estimation error is up to 27 times smaller than the LSEE error which is realized for a small, high-contrast region, for example the aorta. In estimating the regional resistivities, the MiMSEE algorithm requires 25.8 (for the five-region resistivity distribution) and 22.2 (for the six-region resistivity distribution) times more computational time than the LSEE. This gap between the computational times of the two algorithms decreases are the number of regions increases.