Chinchapatnam Phani, Rhode Kawal S, Ginks Matthew, Rinaldi C Aldo, Lambiase Pier, Razavi Reza, Arridge Simon, Sermesant Maxime
Centre for Medical Image Computing, University College London, Gower Street, WC1E 6BT London, U.K.
IEEE Trans Med Imaging. 2008 Nov;27(11):1631-42. doi: 10.1109/TMI.2008.2004644.
We present an adaptive algorithm which uses a fast electrophysiological (EP) model to estimate apparent electrical conductivity and local conduction velocity from noncontact mapping of the endocardial surface potential. Development of such functional imaging revealing hidden parameters of the heart can be instrumental for improved diagnosis and planning of therapy for cardiac arrhythmia and heart failure, for example during procedures such as radio-frequency ablation and cardiac resynchronisation therapy. The proposed model is validated on synthetic data and applied to clinical data derived using hybrid X-ray/magnetic resonance imaging. We demonstrate a qualitative match between the estimated conductivity parameter and pathology locations in the human left ventricle. We also present a proof of concept for an electrophysiological model which utilizes the estimated apparent conductivity parameter to simulate the effect of pacing different ventricular sites. This approach opens up possibilities to directly integrate modelling in the cardiac EP laboratory.
我们提出了一种自适应算法，该算法使用快速电生理（EP）模型，根据心内膜表面电位的非接触映射来估计表观电导率和局部传导速度。开发这种揭示心脏隐藏参数的功能成像，对于改善心律失常和心力衰竭的诊断及治疗规划可能具有重要作用，例如在射频消融和心脏再同步治疗等手术过程中。所提出的模型在合成数据上得到了验证，并应用于使用混合X射线/磁共振成像获得的临床数据。我们展示了估计的电导率参数与人类左心室病理位置之间的定性匹配。我们还提出了一个电生理模型的概念验证，该模型利用估计的表观电导率参数来模拟不同心室部位起搏的效果。这种方法为在心脏电生理实验室中直接整合建模开辟了可能性。
