collaborators

5 papers

cs.LG2021

Learning atrial fiber orientations and conductivity tensors from intracardiac maps using physics-informed neural networks

Thomas Grandits, Simone Pezzuto, Francisco Sahli Costabal +4

Electroanatomical maps are a key tool in the diagnosis and treatment of atrial fibrillation. Current approaches focus on the activation times recorded. However, more information ca…

math.OC2021

GEASI: Geodesic-based Earliest Activation Sites Identification in cardiac models

Thomas Grandits, Alexander Effland, Thomas Pock +3

The identification of the initial ventricular activation sequence is a critical step for the correct personalization of patient-specific cardiac models. In healthy conditions, the…

math.NA2021

Fast and Accurate Uncertainty Quantification for the ECG with Random Electrodes Location

Michael Multerer, Simone Pezzuto

The standard electrocardiogram (ECG) is a point-wise evaluation of the body potential at certain given locations. These locations are subject to uncertainty and may vary from patie…

math.NA2020

Space-time shape uncertainties in the forward and inverse problem of electrocardiography

Lia Gander, Rolf Krause, Michael Multerer +1

In electrocardiography, the "classic" inverse problem is the reconstruction of electric potentials at a surface enclosing the heart from remote recordings at the body surface and a…

math.OC2020

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

Thomas Grandits, Simone Pezzuto, Jolijn M. Lubrecht +3

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 ther…