87 citations · 182 across the 19 of their papers we have counts for
20 papers
DREAM: Deep-Reparametrization of Adaptive Regularization Maps for Fast Zero-Shot Self-Supervised Learning
Thanh Trung Vu, Ander Biguri, Christoph Kolbitsch +3
Adaptive regularization is an effective means of improving the flexibility of classical variational reconstruction methods while retaining their interpretability and mathematical s…
Learning spatially varying regularisation parameters of low regularity for image reconstruction
Kostas Papafitsoros, Luca Calatroni, Andreas Kofler
In this chapter, we review and discuss the regularity properties of spatially adaptive regularisation weight functions used in variational image reconstruction. Incorporating such…
Learning spatially adaptive sparsity level maps for arbitrary convolutional dictionaries
Joshua Schulz, David Schote, Christoph Kolbitsch +2
State-of-the-art learned reconstruction methods often rely on black-box modules that, despite their strong performance, raise questions about their interpretability and robustness.…
MRpro: open framework for model-based, learned, and quantitative MR imaging
Felix Frederik Zimmermann, Patrick Schuenke, Christoph S. Aigner +12
We preseent an open-source image reconstruction package built upon PyTorch, enabling modern deep-learning reconstructions. It uses open data formats for input and output (ISMRMRD,…
Deep unrolling for learning optimal spatially varying regularisation parameters for Total Generalised Variation
Thanh Trung Vu, Andreas Kofler, Kostas Papafitsoros
We extend a recently introduced deep unrolling framework for learning spatially varying regularisation parameters in inverse imaging problems to the case of Total Generalised Varia…
Learning Spatially Adaptive -Norms Weights for Convolutional Synthesis Regularization
Andreas Kofler, Luca Calatroni, Christoph Kolbitsch +1
We propose an unrolled algorithm approach for learning spatially adaptive parameter maps in the framework of convolutional synthesis-based regularization. More precisely,…
MR imaging in the low-field: Leveraging the power of machine learning
Andreas Kofler, Dongyue Si, David Schote +3
Recent innovations in Magnetic Resonance Imaging (MRI) hardware and software have reignited interest in low-field () and ultra-low-field MRI (). T…
Machine Learning for Quantitative MR Image Reconstruction
Andreas Kofler, Felix Frederik Zimmermann, Kostas Papafitsoros
In the last years, the design of image reconstruction methods in the field of quantitative Magnetic Resonance Imaging (qMRI) has experienced a paradigm shift. Often, when dealing w…
NoSENSE: Learned unrolled cardiac MRI reconstruction without explicit sensitivity maps
Felix Frederik Zimmermann, Andreas Kofler
We present a novel learned image reconstruction method for accelerated cardiac MRI with multiple receiver coils based on deep convolutional neural networks (CNNs) and algorithm unr…
Quantitative MR Image Reconstruction using Parameter-Specific Dictionary Learning with Adaptive Dictionary-Size and Sparsity-Level Choice
Andreas Kofler, Kirsten Miriam Kerkering, Laura Göschel +2
Objective: We propose a method for the reconstruction of parameter-maps in Quantitative Magnetic Resonance Imaging (QMRI). Methods: Because different quantitative parameter-maps di…
PINQI: An End-to-End Physics-Informed Approach to Learned Quantitative MRI Reconstruction
Felix F Zimmermann, Christoph Kolbitsch, Patrick Schuenke +1
Quantitative Magnetic Resonance Imaging (qMRI) enables the reproducible measurement of biophysical parameters in tissue. The challenge lies in solving a nonlinear, ill-posed invers…
Unrolled three-operator splitting for parameter-map learning in Low Dose X-ray CT reconstruction
Andreas Kofler, Fabian Altekrüger, Fatima Antarou Ba +6
We propose a method for fast and automatic estimation of spatially dependent regularization maps for total variation-based (TV) tomography reconstruction. The estimation is based o…
Learning Regularization Parameter-Maps for Variational Image Reconstruction using Deep Neural Networks and Algorithm Unrolling
Andreas Kofler, Fabian Altekrüger, Fatima Antarou Ba +6
We introduce a method for fast estimation of data-adapted, spatio-temporally dependent regularization parameter-maps for variational image reconstruction, focusing on total variati…
Convolutional Dictionary Learning by End-To-End Training of Iterative Neural Networks
Andreas Kofler, Christian Wald, Tobias Schaeffter +2
Sparsity-based methods have a long history in the field of signal processing and have been successfully applied to various image reconstruction problems. The involved sparsifying t…
Convolutional Analysis Operator Learning by End-To-End Training of Iterative Neural Networks
Andreas Kofler, Christian Wald, Tobias Schaeffter +2
The concept of sparsity has been extensively applied for regularization in image reconstruction. Typically, sparsifying transforms are either pre-trained on ground-truth images or…
An End-To-End-Trainable Iterative Network Architecture for Accelerated Radial Multi-Coil 2D Cine MR Image Reconstruction
Andreas Kofler, Markus Haltmeier, Tobias Schaeffter +1
Purpose: Iterative Convolutional Neural Networks (CNNs) which resemble unrolled learned iterative schemes have shown to consistently deliver state-of-the-art results for image reco…
Unsupervised Adaptive Neural Network Regularization for Accelerated Radial Cine MRI
Andreas Kofler, Marc Dewey, Tobias Schaeffter +2
In this work, we propose an iterative reconstruction scheme (ALONE - Adaptive Learning Of NEtworks) for 2D radial cine MRI based on ground truth-free unsupervised learning of shall…
Neural Networks-based Regularization for Large-Scale Medical Image Reconstruction
Andreas Kofler, Markus Haltmeier, Tobias Schaeffter +4
In this paper we present a generalized Deep Learning-based approach for solving ill-posed large-scale inverse problems occuring in medical image reconstruction. Recently, Deep Lear…
Spatio-Temporal Deep Learning-Based Undersampling Artefact Reduction for 2D Radial Cine MRI with Limited Data
Andreas Kofler, Marc Dewey, Tobias Schaeffter +2
In this work we reduce undersampling artefacts in two-dimensional () golden-angle radial cine cardiac MRI by applying a modified version of the U-net. We train the network on $…
A splitting/polynomial chaos expansion approach for stochastic evolution equations
Andreas Kofler, Tijana Levajković, Hermann Mena +1
In this paper, we combine deterministic splitting methods with a polynomial chaos expansion method for solving stochastic parabolic evolution problems. The stochastic differential…