555 citations · 1.7k across the 17 of their papers we have counts for
18 papers
Stable Deep MRI Reconstruction using Generative Priors
Martin Zach, Florian Knoll, Thomas Pock
Data-driven approaches recently achieved remarkable success in magnetic resonance imaging (MRI) reconstruction, but integration into clinical routine remains challenging due to a l…
Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging
Kerstin Hammernik, Thomas Küstner, Burhaneddin Yaman +4
Physics-driven deep learning methods have emerged as a powerful tool for computational magnetic resonance imaging (MRI) problems, pushing reconstruction performance to new limits.…
Alternating Learning Approach for Variational Networks and Undersampling Pattern in Parallel MRI Applications
Marcelo V. W. Zibetti, Florian Knoll, Ravinder R. Regatte
Purpose: To propose an alternating learning approach to learn the sampling pattern (SP) and the parameters of variational networks (VN) in accelerated parallel magnetic resonance i…
fastMRI+: Clinical Pathology Annotations for Knee and Brain Fully Sampled Multi-Coil MRI Data
Ruiyang Zhao, Burhaneddin Yaman, Yuxin Zhang +7
Improving speed and image quality of Magnetic Resonance Imaging (MRI) via novel reconstruction approaches remains one of the highest impact applications for deep learning in medica…
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,…
Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction
Matthew J. Muckley, Bruno Riemenschneider, Alireza Radmanesh +20
Accelerating MRI scans is one of the principal outstanding problems in the MRI research community. Towards this goal, we hosted the second fastMRI competition targeted towards reco…
CG-SENSE revisited: Results from the first ISMRM reproducibility challenge
Oliver Maier, Steven H. Baete, Alexander Fyrdahl +11
Purpose: The aim of this work is to shed light on the issue of reproducibility in MR image reconstruction in the context of a challenge. Participants had to recreate the results of…
End-to-End Variational Networks for Accelerated MRI Reconstruction
Anuroop Sriram, Jure Zbontar, Tullie Murrell +5
The slow acquisition speed of magnetic resonance imaging (MRI) has led to the development of two complementary methods: acquiring multiple views of the anatomy simultaneously (para…
Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge
Florian Knoll, Tullie Murrell, Anuroop Sriram +8
Purpose: To advance research in the field of machine learning for MR image reconstruction with an open challenge. Methods: We provided participants with a dataset of raw k-space da…
Training a Neural Network for Gibbs and Noise Removal in Diffusion MRI
Matthew J. Muckley, Benjamin Ades-Aron, Antonios Papaioannou +7
We develop and evaluate a neural network-based method for Gibbs artifact and noise removal. A convolutional neural network (CNN) was designed for artifact removal in diffusion-weig…
Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction
Florian Knoll, Kerstin Hammernik, Chi Zhang +4
Following the success of deep learning in a wide range of applications, neural network-based machine learning techniques have received interest as a means of accelerating magnetic…
fastMRI: An Open Dataset and Benchmarks for Accelerated MRI
Jure Zbontar, Florian Knoll, Anuroop Sriram +24
Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measurements has the potential to reduce medical costs, minimize stress to patients and make MRI possible in applicati…
Multicompartment Magnetic Resonance Fingerprinting
Sunli Tang, Carlos Fernandez-Granda, Sylvain Lannuzel +5
Magnetic resonance fingerprinting (MRF) is a technique for quantitative estimation of spin-relaxation parameters from magnetic-resonance data. Most current MRF approaches assume th…
Coupled regularization with multiple data discrepancies
Martin Holler, Richard Huber, Florian Knoll
We consider a class of regularization methods for inverse problems where a coupled regularization is employed for the simultaneous reconstruction of data from multiple sources. App…
Learning a Variational Network for Reconstruction of Accelerated MRI Data
Kerstin Hammernik, Teresa Klatzer, Erich Kobler +4
Purpose: To allow fast and high-quality reconstruction of clinical accelerated multi-coil MR data by learning a variational network that combines the mathematical structure of vari…
Low Rank Alternating Direction Method of Multipliers Reconstruction for MR Fingerprinting
Jakob Assländer, Martijn A Cloos, Florian Knoll +3
Purpose The proposed reconstruction framework addresses the reconstruction accuracy, noise propagation and computation time for Magnetic Resonance Fingerprinting (MRF). Methods Bas…
Preconditioned ADMM with nonlinear operator constraint
Martin Benning, Florian Knoll, Carola-Bibiane Schönlieb +1
We are presenting a modification of the well-known Alternating Direction Method of Multipliers (ADMM) algorithm with additional preconditioning that aims at solving convex optimisa…
Fast T2 Mapping with Improved Accuracy Using Undersampled Spin-echo MRI and Model-based Reconstructions with a Generating Function
Tilman J. Sumpf, Andreas Petrovic, Martin Uecker +2
A model-based reconstruction technique for accelerated T2 mapping with improved accuracy is proposed using undersampled Cartesian spin-echo MRI data. The technique employs an advan…