313 citations · 332 across the 3 of their papers we have counts for
3 papers · 1 filter
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.…
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…
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…