Influence of Modeling Errors on the Initial Estimate for Nonlinear Myocardial Activation Times Imaging Calculated With Fastest Route Algorithm
Influence of Modeling Errors on the Initial Estimate for Nonlinear Myocardial Activation Times Imaging Calculated With Fastest Route Algorithm
复制标题
DOI：
10.1109/tbme.2016.2561973
复制
发表时间：
2016-12-01
影响因子：
4.6
通讯作者：
van Dam, Peter
中科院分区：
文献类型：
--
作者：
Potyagaylo, Danila;Doessel, Olaf;van Dam, Peter
心脏电活动的非侵入性重建具有支持临床决策、计划和治疗的巨大潜力。近年来，以等效双层（EDL）为源模型的边界元法（BEM）在体表电位图（BSPM）估计心脏激动方面取得了重大进展。在这个公式中，激活时间的非侵入性评估结果在一个非线性优化问题与最快路线算法（FRA）计算的初始估计。每个FRA模拟的激活序列被转换为ECG。最佳初始化由提供预测电位与测量电位之间的最高相关性的序列确定。我们定量评估的影响，基于FRA的初始化的正演模拟误差。我们提出了三个模拟设置，调查体积导体模型简化的影响，忽略心脏各向异性和几何误差的异位搏动的定位开始在心室表面上。采用12导联心电图和99电极BSPM系统进行分析。心脏中暴露最大定位误差的区域是体积导体模型和电极配置特定的，平均误差
Noninvasive reconstruction of cardiac electrical activity has a great potential to support clinical decision making, planning, and treatment. Recently, significant progress has been made in the estimation of the cardiac activation from body surface potential maps (BSPMs) using boundary element method (BEM) with the equivalent double layer (EDL) as a source model. In this formulation, noninvasive assessment of activation times results in a nonlinear optimization problem with an initial estimate calculated with the fastest route algorithm (FRA). Each FRA-simulated activation sequence is converted into the ECG. The best initialization is determined by the sequence providing the highest correlation between predicted and measured potentials. We quantitatively assess the effects of the forward modeling errors on the FRA-based initialization. We present three simulation setups to investigate the effects of volume conductor model simplifications, neglecting the cardiac anisotropy and geometrical errors on the localization of ectopic beats starting on the ventricular surface. For the analysis, 12-lead ECG and 99 electrodes BSPM system were used. The areas in the heart exposing the largest localization errors were volume conductor model and electrode configuration specific with an average error