Abstract
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.
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Keywords
- Left Bundle Branch Block
- Eikonal Equation
- Adjoint Method
- Local Conductivity
- Parameter Estimation Procedure
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© 2005 Springer-Verlag Berlin Heidelberg
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Sermesant, M., Coudière, Y., Moreau-Villéger, V., Rhode, K.S., Hill, D.L.G., Razavi, R.S. (2005). A Fast-Marching Approach to Cardiac Electrophysiology Simulation for XMR Interventional Imaging. In: Duncan, J.S., Gerig, G. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2005. MICCAI 2005. Lecture Notes in Computer Science, vol 3750. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11566489_75
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DOI: https://doi.org/10.1007/11566489_75
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29326-2
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