End-to-End Variational Networks for Accelerated MRI Reconstruction

Anuroop Sriram, Jure Zbontar, Tullie Murrell, Aaron Defazio, C. Lawrence Zitnick +3 more
4/14/2020
eess.IVcs.CV

Official code: facebookresearch/fastMRI (1,537 stars, MIT, PyTorch, last commit 2025-01-21).

Abstract

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 (parallel imaging) and acquiring fewer samples than necessary for traditional signal processing methods (compressed sensing). While the combination of these methods has the potential to allow much faster scan times, reconstruction from such undersampled multi-coil data has remained an open problem. In this paper, we present a new approach to this problem that extends previously proposed variational methods by learning fully end-to-end. Our method obtains new state-of-the-art results on the fastMRI dataset for both brain and knee MRIs.

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Code Implementations(1)

facebookresearch/fastMRIOfficial100%
PyTorchMIT

A large-scale dataset of both raw MRI measurements and clinical MRI images.

1,537422PythonOct 26, 20181 years agoMIT
convolutional-neural-networksdeep-learningfastmrifastmri-challengefastmri-dataset+4 more

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Cite this paper

@article{sriram2020endtoend,
  title  = {End-to-End Variational Networks for Accelerated MRI Reconstruction},
  author = {Anuroop Sriram and Jure Zbontar and Tullie Murrell and Aaron Defazio and C. Lawrence Zitnick and Nafissa Yakubova and Florian Knoll and Patricia Johnson},
  year   = {2020},
  eprint = {2004.06688},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/2004.06688}
}

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