Sermesant M, Coudière Y, Moreau-Villèger V, Rhode K S, Hill D L G, Razavi R S
Division of Imaging Sciences, King's College London, 5th floor Thomas Guy House, Guy's Hospital, London, UK.
Med Image Comput Comput Assist Interv. 2005;8(Pt 2):607-15. doi: 10.1007/11566489_75.
Cardiac ablation procedures are becoming more routine to treat arrhythmias. The development of electrophysiological models will allow investigation of treatment strategies. However, current models are computationally expensive and often too complex to be adjusted with current clinical data. In this paper, we have proposed a fast algorithm to solve Eikonal-based models on triangular meshes. These models can be used to extract hidden parameters of the cardiac function from clinical data in a very short time, thus could be used during interventions. We propose a first approach to estimate these parameters, and have tested it on synthetic and real data derived using XMR imaging. We demonstrated a qualitative matching between the estimated parameter and XMR data. This novel approach opens up possibilities to directly integrate modelling in the interventional room.
心脏消融手术正日益成为治疗心律失常的常规手段。电生理模型的发展将有助于研究治疗策略。然而，当前的模型计算成本高昂，且往往过于复杂，难以根据当前临床数据进行调整。在本文中，我们提出了一种快速算法，用于在三角形网格上求解基于程函方程的模型。这些模型可用于在极短时间内从临床数据中提取心脏功能的隐藏参数，从而可在介入治疗期间使用。我们提出了一种估计这些参数的初步方法，并在使用X射线磁共振成像（XMR）获得的合成数据和真实数据上进行了测试。我们证明了估计参数与XMR数据之间的定性匹配。这种新方法为在介入手术室直接整合建模开辟了可能性。
