Electroanatomical mapping, a keystone diagnostic tool in cardiac
electrophysiology studies, can provide high-density maps of the local electric
properties of the tissue. It is therefore tempting to use such data to better
individualize current patient-specific models of the heart through a data
assimilation procedure and to extract potentially insightful information such
as conduction properties. Parameter identification for state-of-the-art cardiac
models is however a challenging task. In this work, we introduce a novel
inverse problem for inferring the anisotropic structure of the conductivity
tensor, that is fiber orientation and conduction velocity along and across
fibers, of an eikonal model for cardiac activation. The proposed method, named
PIEMAP, performed robustly with synthetic data and showed promising results
with clinical data. These results suggest that PIEMAP could be a useful
supplement in future clinical workflows of personalized therapies.Comment: 12 pages, 4 figures, 1 tabl