Wang Linwei, Zhang Heye, Shi Pengcheng, Liu Huafeng
Department of Electrical and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong.
Med Image Comput Comput Assist Interv. 2006;9(Pt 1):792-9. doi: 10.1007/11866565_97.
We present a model-based framework for imaging 3D cardiac transmembrane potential (TMP) distributions from body surface potential (BSP) measurements. Based on physiologically motivated modeling of the spatiotemporal evolution of TMPs and their projection to body surface, the cardiac electrophysiology is modeled as a stochastic system with TMPs as the latent dynamics and BSPs as external measurements. Given the patient-specific data from BSP measurements and tomographic medical images, the inverse problem of electrocardiography (IECG) is solved via state estimation of the underlying system, using the unscented Kalman filtering (UKF) for data assimilation. By incorporating comprehensive a priori physiological information, the framework enables direct recovery of intracardiac electrophysiological events free from commonly used physical equivalent cardiac sources, and delivers accurate, robust, and fast converging results under different noise levels and types. Experiments concerning individual variances and pathologies are also conducted to verify its feasibility in patient-specific applications.
我们提出了一个基于模型的框架，用于从体表电位（BSP）测量中成像三维心脏跨膜电位（TMP）分布。基于TMPs时空演变的生理驱动模型及其向体表的投影，心脏电生理学被建模为一个随机系统，其中TMPs为潜在动态，BSPs为外部测量值。给定来自BSP测量和断层医学图像的患者特定数据，通过对基础系统的状态估计来解决心电图逆问题（IECG），使用无迹卡尔曼滤波（UKF）进行数据同化。通过纳入全面的先验生理信息，该框架能够直接恢复心内电生理事件，而无需常用的物理等效心脏源，并在不同噪声水平和类型下提供准确、稳健且快速收敛的结果。还进行了关于个体差异和病理情况的实验，以验证其在患者特定应用中的可行性。
