Zobrazeno 1 - 10
of 46
pro vyhledávání: '"Costabal, Francisco Sahli"'
Recent works have shown that traditional Neural Network (NN) architectures display a marked frequency bias in the learning process. Namely, the NN first learns the low-frequency features before learning the high-frequency ones. In this study, we rigo
Externí odkaz:
http://arxiv.org/abs/2405.14957
Autor:
Spieker, Veronika, Eichhorn, Hannah, Stelter, Jonathan K., Huang, Wenqi, Braren, Rickmer F., Rückert, Daniel, Costabal, Francisco Sahli, Hammernik, Kerstin, Prieto, Claudia, Karampinos, Dimitrios C., Schnabel, Julia A.
Neural implicit k-space representations have shown promising results for dynamic MRI at high temporal resolutions. Yet, their exclusive training in k-space limits the application of common image regularization methods to improve the final reconstruct
Externí odkaz:
http://arxiv.org/abs/2404.08350
Autor:
Álvarez-Barrientos, Felipe, Salinas-Camus, Mariana, Pezzuto, Simone, Costabal, Francisco Sahli
The identification of the Purkinje conduction system in the heart is a challenging task, yet essential for a correct definition of cardiac digital twins for precision cardiology. Here, we propose a probabilistic approach for identifying the Purkinje
Externí odkaz:
http://arxiv.org/abs/2312.09887
Autor:
Tac, Vahidullah, Rausch, Manuel K, Bilionis, Ilias, Costabal, Francisco Sahli, Tepole, Adrian Buganza
Many natural materials exhibit highly complex, nonlinear, anisotropic, and heterogeneous mechanical properties. Recently, it has been demonstrated that data-driven strain energy functions possess the flexibility to capture the behavior of these compl
Externí odkaz:
http://arxiv.org/abs/2310.03745
Autor:
Verhülsdonk, Jan, Grandits, Thomas, Costabal, Francisco Sahli, Pinetz, Thomas, Krause, Rolf, Auricchio, Angelo, Haase, Gundolf, Pezzuto, Simone, Effland, Alexander
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 statistical models, allowing no advanced topological changes, or requiri
Externí odkaz:
http://arxiv.org/abs/2308.16568
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. This is especially useful in he
Externí odkaz:
http://arxiv.org/abs/2308.00927
Autor:
Catalán, Tabita, Courdurier, Matías, Osses, Axel, Botnar, René, Costabal, Francisco Sahli, Prieto, Claudia
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. Several regularization approaches that
Externí odkaz:
http://arxiv.org/abs/2307.14363
Autor:
López, Pablo Arratia, Mella, Hernán, Uribe, Sergio, Hurtado, Daniel E., Costabal, Francisco Sahli
Heart failure is typically diagnosed with a global function assessment, such as ejection fraction. However, these metrics have low discriminate power, failing to distinguish different types of this disease. Quantifying local deformations in the form
Externí odkaz:
http://arxiv.org/abs/2211.12549
Physics-informed neural networks (PINNs) have demonstrated promise in solving forward and inverse problems involving partial differential equations. Despite recent progress on expanding the class of problems that can be tackled by PINNs, most of exis
Externí odkaz:
http://arxiv.org/abs/2209.03984
We propose a method for identifying an ectopic activation in the heart non-invasively. Ectopic activity in the heart can trigger deadly arrhythmias. The localization of the ectopic foci or earliest activation sites (EASs) is therefore a critical info
Externí odkaz:
http://arxiv.org/abs/2203.06222