Abstract
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 workflowss of personalized therapies.
This research was supported by the grants F3210-N18 and I2760-B30 from the Austrian Science Fund (FWF) and BioTechMed Graz flagship award “ILearnHeart”, as well as ERC Starting grant HOMOVIS, No. 640156. This work was also financially supported by the Theo Rossi di Montelera Foundation, the Metis Foundation Sergio Mantegazza, the Fidinam Foundation, the Horten Foundation and the CSCS–Swiss National Supercomputing Centre production grant s778.
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Grandits, T., Pezzuto, S., Lubrecht, J.M., Pock, T., Plank, G., Krause, R. (2021). PIEMAP: Personalized Inverse Eikonal Model from Cardiac Electro-Anatomical Maps. In: Puyol Anton, E., et al. Statistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges. STACOM 2020. Lecture Notes in Computer Science(), vol 12592. Springer, Cham. https://doi.org/10.1007/978-3-030-68107-4_8
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