944 citations · 3.4k across the 10 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…
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…
Simulating single-coil MRI from the responses of multiple coils
Mark Tygert, Jure Zbontar
We convert the information-rich measurements of parallel and phased-array MRI into noisier data that a corresponding single-coil scanner could have taken. Specifically, we replace…
Compressed sensing with a jackknife and a bootstrap
Mark Tygert, Rachel Ward, Jure Zbontar
Compressed sensing proposes to reconstruct more degrees of freedom in a signal than the number of values actually measured. Compressed sensing therefore risks introducing errors --…