1 code implementation • 15 Dec 2023 • Felipe Álvarez-Barrientos, Mariana Salinas-Camus, Simone Pezzuto, Francisco Sahli Costabal
Our methodology is a step forward in creation of digital twins from non-invasive data in precision medicine.
no code implementations • 31 Aug 2023 • Jan Verhülsdonk, Thomas Grandits, Francisco Sahli Costabal, Rolf Krause, Angelo Auricchio, Gundolf Haase, Simone Pezzuto, Alexander Effland
The efficient construction of an anatomical model is one of the major challenges of patient-specific in-silico models of the human heart.
no code implementations • 16 Aug 2023 • Thomas Grandits, Jan Verhülsdonk, Gundolf Haase, Alexander Effland, Simone Pezzuto
The eikonal equation has become an indispensable tool for modeling cardiac electrical activation accurately and efficiently.
1 code implementation • 8 Sep 2022 • Francisco Sahli Costabal, Simone Pezzuto, Paris Perdikaris
We approximate the eigenfunctions as well as the operators involved in the partial differential equations with finite elements.
no code implementations • 11 Mar 2022 • Simone Pezzuto, Paris Perdikaris, Francisco Sahli Costabal
We propose a method for identifying an ectopic activation in the heart non-invasively.
1 code implementation • 28 Jan 2022 • Carlos Ruiz Herrera, Thomas Grandits, Gernot Plank, Paris Perdikaris, Francisco Sahli Costabal, Simone Pezzuto
The inverse problem amounts to identifying the conduction velocity tensor of a cardiac propagation model from a set of sparse activation maps.
no code implementations • 12 Jan 2022 • Thomas Grandits, Simone Pezzuto, Gernot Plank
The field of cardiac electrophysiology tries to abstract, describe and finally model the electrical characteristics of a heartbeat.
no code implementations • 15 Dec 2021 • Lia Gander, Simone Pezzuto, Ali Gharaviri, Rolf Krause, Paris Perdikaris, Francisco Sahli Costabal
Computational models of atrial fibrillation have successfully been used to predict optimal ablation sites.
no code implementations • 22 Feb 2021 • Thomas Grandits, Simone Pezzuto, Francisco Sahli Costabal, Paris Perdikaris, Thomas Pock, Gernot Plank, Rolf Krause
In this work, we employ a recently developed approach, called physics informed neural networks, to learn the fiber orientations from electroanatomical maps, taking into account the physics of the electrical wave propagation.