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 • 11 Sep 2023 • Vahidullah Tac, Manuel K Rausch, Ilias Bilionis, Francisco Sahli Costabal, Adrian Buganza Tepole
We extend our approach to spatially correlated diffusion resulting in heterogeneous material properties for arbitrary geometries.
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.
1 code implementation • 2 Aug 2023 • Jeremias Garay, Jocelyn Dunstan, Sergio Uribe, Francisco Sahli Costabal
Physics-informed neural networks (PINNs) have emerged as a powerful tool for solving inverse problems, especially in cases where no complete information about the system is known and scatter measurements are available.
no code implementations • 24 Jul 2023 • Tabita Catalán, Matías Courdurier, Axel Osses, René Botnar, Francisco Sahli Costabal, Claudia Prieto
Cardiac cine MRI is the gold standard for cardiac functional assessment, but the inherently slow acquisition process creates the necessity of reconstruction approaches for accelerated undersampled acquisitions.
1 code implementation • 22 Nov 2022 • Pablo Arratia López, Hernán Mella, Sergio Uribe, Daniel E. Hurtado, Francisco Sahli Costabal
In this work, we introduce WarpPINN, a physics-informed neural network to perform image registration to obtain local metrics of the heart deformation.
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 • 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.
1 code implementation • 9 May 2019 • Francisco Sahli Costabal, Paris Perdikaris, Ellen Kuhl, Daniel E. Hurtado
In an application to cardiac electrophysiology, the multi-fidelity classifier achieves an F1 score, the harmonic mean of precision and recall, of 99. 6% compared to 74. 1% of a single-fidelity classifier when both are trained with 50 samples.