19 citations · 64 across the 16 of their papers we have counts for
22 papers
Total Generalized Variation for Inverse Problems Involving Piecewise Linear Finite Elements
Moritz Kappes, Manuel Haas, Thomas Beiert +2
Higher-order regularization has shown improved results over first-order methods such as total variation. In this paper, convergence of a piecewise linear finite element discretizat…
MotionDPS: Motion-Compensated 3D Brain MRI Reconstruction
Antonio Ortiz-Gonzalez, Erich Kobler, Lukas Schletter +1
Magnetic resonance imaging (MRI) is highly susceptible to patient motion due to its relatively long acquisition times and the fact that data are acquired sequentially in k-space. E…
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…
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…
Gadolinium dose reduction for brain MRI using conditional deep learning
Thomas Pinetz, Erich Kobler, Robert Haase +7
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 pre…
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…
Faithful Synthesis of Low-dose Contrast-enhanced Brain MRI Scans using Noise-preserving Conditional GANs
Thomas Pinetz, Erich Kobler, Robert Haase +3
Today Gadolinium-based contrast agents (GBCA) are indispensable in Magnetic Resonance Imaging (MRI) for diagnosing various diseases. However, GBCAs are expensive and may accumulate…
Learned Discretization Schemes for the Second-Order Total Generalized Variation
Lea Bogensperger, Antonin Chambolle, Alexander Effland +1
The total generalized variation extends the total variation by incorporating higher-order smoothness. Thus, it can also suffer from similar discretization issues related to isotrop…
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…
Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction
Dominik Narnhofer, Alexander Effland, Erich Kobler +3
Recent deep learning approaches focus on improving quantitative scores of dedicated benchmarks, and therefore only reduce the observation-related (aleatoric) uncertainty. However,…
Shared Prior Learning of Energy-Based Models for Image Reconstruction
Thomas Pinetz, Erich Kobler, Thomas Pock +1
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-bas…
Total Deep Variation: A Stable Regularizer for Inverse Problems
Erich Kobler, Alexander Effland, Karl Kunisch +1
Various problems in computer vision and medical imaging can be cast as inverse problems. A frequent method for solving inverse problems is the variational approach, which amounts t…
Total Deep Variation for Linear Inverse Problems
Erich Kobler, Alexander Effland, Karl Kunisch +1
Diverse inverse problems in imaging can be cast as variational problems composed of a task-specific data fidelity term and a regularization term. In this paper, we propose a novel…
Consistent Curvature Approximation on Riemannian Shape Spaces
Alexander Effland, Behrend Heeren, Martin Rumpf +1
We describe how to approximate the Riemann curvature tensor as well as sectional curvatures on possibly infinite-dimensional shape spaces that can be thought of as Riemannian manif…
Image Morphing in Deep Feature Spaces: Theory and Applications
Alexander Effland, Erich Kobler, Thomas Pock +2
This paper combines image metamorphosis with deep features. To this end, images are considered as maps into a high-dimensional feature space and a structure-sensitive, anisotropic…
An Optimal Control Approach to Early Stopping Variational Methods for Image Restoration
Alexander Effland, Erich Kobler, Karl Kunisch +1
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 b…
Convergence of the Time Discrete Metamorphosis Model on Hadamard Manifolds
Alexander Effland, Sebastian Neumayer, Martin Rumpf
Continuous image morphing is a classical task in image processing. The metamorphosis model proposed by Trouvé, Younes and coworkers casts this problem in the frame of Riemannian ge…
Image Extrapolation for the Time Discrete Metamorphosis Model - Existence and Applications
Alexander Effland, Martin Rumpf, Florian Schäfer
The space of images can be equipped with a Riemannian metric measuring both the cost of transport of image intensities and the variation of image intensities along motion lines. Th…
A Posteriori Error Control for the Binary Mumford-Shah Model
Benjamin Berkels, Alexander Effland, Martin Rumpf
The binary Mumford-Shah model is a widespread tool for image segmentation and can be considered as a basic model in shape optimization with a broad range of applications in compute…
Bézier curves in the space of images
Alexander Effland, Martin Rumpf, Stefan Simon +2
Bézier curves are a widespread tool for the design of curves in Euclidian space. This paper generalizes the notion of Bézier curves to the infinite-dimensional space of images. To…
Time Discrete Geodesic Paths in the Space of Images
Benjamin Berkels, Alexander Effland, Martin Rumpf
In this paper the space of images is considered as a Riemannian manifold using the metamorphosis approach, where the underlying Riemannian metric simultaneously measures the cost o…