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Electroanatomical mapping, a keystone diagnostic tool in cardiacelectrophysiology studies, can provide high-density maps of the local electricproperties of the tissue. It is therefore tempting to use such data to betterindividualize current patient-specific models of the heart through a dataassimilation procedure and to extract potentially insightful information suchas conduction properties. Parameter identification for state-of-the-art cardiacmodels is however a challenging task. In this work, we introduce a novelinverse problem for inferring the anisotropic structure of the conductivitytensor, that is fiber orientation and conduction velocity along and acrossfibers, of an eikonal model for cardiac activation. The proposed method, namedPIEMAP, performed robustly with synthetic data and showed promising resultswith clinical data. These results suggest that PIEMAP could be a usefulsupplement in future clinical workflows of personalized therapies.

PIEMAP: Personalized Inverse Eikonal Model from cardiac Electro-Anatomical Maps