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2019

i-RIM applied to the fastMRI challenge

Putzky, Patrick, Karkalousos, Dimitrios, Teuwen, Jonas et al.

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We, team AImsterdam, summarize our submission to the fastMRI challenge (Zbontar et al., 2018).

Our approach builds on recent advances in invertible learning to infer models as presented in Putzky and Welling (2019).
Both, our single-coil and our multi-coil model share the same basic architecture.

i-RIM applied to the fastMRI challenge · Around

Built on

Image quality assessment: form error visibility to structural similarity
Original
Zhou Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli · 2004
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Deep residual learning for image recognition
Original
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Recurrent inference machines for solving inverse problems
Original
Patrick Putzky and Max Welling · 2017
Earlier work this paper cites.

Similar

The reversible residual network: Backpropagation without storing activations
Aidan N Gomez, Mengye Ren, Raquel Urtasun, and Roger B Grosse · 2017
Cited alongside, same era.
FastMRI: An open dataset and benchmarks for accelerated MRI
Original
Jure Zbontar, Florian Knoll, Anuroop Sriram, Matthew J Muckley, Mary Bruno, Aaron Defazio, Marc Parente, Krzysztof J Geras, Joe Katsnelson, Hersh Chandarana, et al · 2018
Cited alongside, same era.

Then

Invert to learn to invert
Patrick Putzky and Max Welling · 2019
Closest in time.
Recurrent inference machines for reconstructing heterogeneous MRI data
Kai Lønning, Patrick Putzky, Jan-Jakob Sonke, Liesbeth Reneman, Matthan W.A. Caan, and Max Welling · 2019
Closest in time.

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