Abstract
Sudden cardiac death is a major cause of death in industrialized world; in particular, patients with prior infarction can develop lethal arrhythmia. Our aim is to understand the transmural propagation of electrical wave and to accurately predict activation times under different stimulation conditions (sinus rhythm and paced) using MRI-based computer models of normal or structurally diseased hearts. Parameterization of such models is a prerequisite step prior integration into clinical platforms. In this work, we first evaluated the errors associated with the registration process between contact EP data and MRI-based models, using in vivo CARTO maps recorded in three swine hearts (two healthy and one infarcted) and the corresponding heart meshes obtained from high-resolution ex vivo diffusion weighted DW-MRI (voxel size < 1mm3). We used the open-source software Vurtigo to align, register and project the CARTO depolarization maps (from LV-endocardium and epicardium) onto the MR-derived meshes, with an acceptable registration error of < 5mm in all maps. We then compared simulation results obtained with the macroscopic monodomain formalism (i.e., the two-variable Aliev-Panfilov model), the simple Eikonal model, and the complex bidomain model (TNNP model) under different stimulation conditions. We found small errors between the measured and the predicted activation times, as well as between the depolarization times using these three models (e.g., with a mean error of 3.4 ms between the A-P and TNNP model), suggesting that simple mathematical formalisms might be a good choice for integration of fast, predictive models into clinical platforms.
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Pop, M. et al. (2013). In vivo Contact EP Data and ex vivo MR-Based Computer Models: Registration and Model-Dependent Errors. In: Camara, O., Mansi, T., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds) Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges. STACOM 2012. Lecture Notes in Computer Science, vol 7746. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36961-2_41
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DOI: https://doi.org/10.1007/978-3-642-36961-2_41
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