19 citations · 51 across the 12 of their papers we have counts for
12 papers
Uncertainty Quantification for Cardiac Shape Reconstruction with Deep Signed Distance Functions via MCMC methods
Jan Verhülsdonk, Thomas Grandits, Francisco Sahli Costabal +3
Atlas-based approaches allow high-quality, patient-specific shape reconstructions of cardiac anatomy from sparse and/or noisy data such as point clouds. However, these methods are…
Learned Finite Element-based Regularization of the Inverse Problem in Electrocardiographic Imaging
Manuel Haas, Thomas Grandits, Thomas Pinetz +3
Electrocardiographic imaging (ECGI) seeks to reconstruct cardiac electrical activity from body-surface potentials noninvasively. However, the associated inverse problem is severely…
Optimal dynamic thermal plant control: A study and benchmark
Thomas Grandits, Stefano Coss, Gundolf Haase
District heating networks play a vital role in thermal energy supply in many countries. Thus, it comes to no surprise that these has been a central role in improving energy efficie…
Accurate and Efficient Cardiac Digital Twin from surface ECGs: Insights into Identifiability of Ventricular Conduction System
Thomas Grandits, Karli Gillette, Gernot Plank +1
Digital twins for cardiac electrophysiology are an enabling technology for precision cardiology. Current forward models are advanced enough to simulate the cardiac electric activit…
Finite element-based space-time total variation-type regularization of the inverse problem in electrocardiographic imaging
Manuel Haas, Thomas Grandits, Thomas Pinetz +3
Reconstructing cardiac electrical activity from body surface electric potential measurements results in the severely ill-posed inverse problem in electrocardiography. Many differen…
Shape of my heart: Cardiac models through learned signed distance functions
Jan Verhülsdonk, Thomas Grandits, Francisco Sahli Costabal +6
The efficient construction of anatomical models is one of the major challenges of patient-specific in-silico models of the human heart. Current methods frequently rely on linear st…
Digital twinning of cardiac electrophysiology models from the surface ECG: a geodesic backpropagation approach
Thomas Grandits, Jan Verhülsdonk, Gundolf Haase +2
The eikonal equation has become an indispensable tool for modeling cardiac electrical activation accurately and efficiently. In principle, by matching clinically recorded and eikon…
Physics-informed neural networks to learn cardiac fiber orientation from multiple electroanatomical maps
Carlos Ruiz Herrera, Thomas Grandits, Gernot Plank +3
We propose FiberNet, a method to estimate \emph{in-vivo} the cardiac fiber architecture of the human atria from multiple catheter recordings of the electrical activation. Cardiac f…
Smoothness and continuity of cost functionals for ECG mismatch computation
Thomas Grandits, Simone Pezzuto, Gernot Plank
The field of cardiac electrophysiology tries to abstract, describe and finally model the electrical characteristics of a heartbeat. With recent advances in cardiac electrophysiolog…
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…
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…
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…