no code implementations • 6 Mar 2024 • Thomas Pinetz, Erich Kobler, Robert Haase, Julian A. Luetkens, Mathias Meetschen, Johannes Haubold, Cornelius Deuschl, Alexander Radbruch, Katerina Deike, Alexander Effland
Recently, deep learning (DL)-based methods have been proposed for the computational reduction of gadolinium-based contrast agents (GBCAs) to mitigate adverse side effects while preserving diagnostic value.
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 • 26 Jun 2023 • Thomas Pinetz, Erich Kobler, Robert Haase, Katerina Deike-Hofmann, Alexander Radbruch, Alexander Effland
Our numerical experiments show that conditional GANs are suitable for generating images at different GBCA dose levels and can be used to augment datasets for virtual contrast models.
no code implementations • 12 Feb 2021 • Dominik Narnhofer, Alexander Effland, Erich Kobler, Kerstin Hammernik, Florian Knoll, Thomas Pock
To this end, we solve the linear inverse problem of undersampled MRI reconstruction in a variational setting.
no code implementations • 12 Nov 2020 • Thomas Pinetz, Erich Kobler, Thomas Pock, Alexander Effland
We propose a novel learning-based framework for image reconstruction particularly designed for training without ground truth data, which has three major building blocks: energy-based learning, a patch-based Wasserstein loss functional, and shared prior learning.
1 code implementation • 15 Jun 2020 • Erich Kobler, Alexander Effland, Karl Kunisch, Thomas Pock
In this work, we combine the variational formulation of inverse problems with deep learning by introducing the data-driven general-purpose total deep variation regularizer.
1 code implementation • CVPR 2020 • Erich Kobler, Alexander Effland, Karl Kunisch, Thomas Pock
Diverse inverse problems in imaging can be cast as variational problems composed of a task-specific data fidelity term and a regularization term.
no code implementations • 19 Jul 2019 • Alexander Effland, Erich Kobler, Karl Kunisch, Thomas Pock
We investigate a well-known phenomenon of variational approaches in image processing, where typically the best image quality is achieved when the gradient flow process is stopped before converging to a stationary point.