26 citations · 32 across the 5 of their papers we have counts for
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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…
MRI Banding Removal via Adversarial Training
Aaron Defazio, Tullie Murrell, Michael P. Recht
MRI images reconstructed from sub-sampled Cartesian data using deep learning techniques often show a characteristic banding (sometimes described as streaking), which is particularl…
Offset Sampling Improves Deep Learning based Accelerated MRI Reconstructions by Exploiting Symmetry
Aaron Defazio
Deep learning approaches to accelerated MRI take a matrix of sampled Fourier-space lines as input and produce a spatial image as output. In this work we show that by careful choice…
GrappaNet: Combining Parallel Imaging with Deep Learning for Multi-Coil MRI Reconstruction
Anuroop Sriram, Jure Zbontar, Tullie Murrell +3
Magnetic Resonance Image (MRI) acquisition is an inherently slow process which has spurred the development of two different acceleration methods: acquiring multiple correlated samp…