Evaluation of the Robustness of Learned MR Image Reconstruction to Systematic Deviations Between Training and Test Data for the Models from the fastMRI Challenge
Johnson PM, Jeong G, Hammernik K, Schlemper J, Qin C, Duan J, Rueckert D, Lee J, Pezzotti N, De Weerdt E, Yousefi S, Elmahdy MS, Van Gemert JHF, Schuelke C, Doneva M, Nielsen T, Kastryulin S, Lelieveldt BPF, Van Osch MJP, Staring M, Chen EZ, Wang P, Chen X, Chen T, Patel VM, Sun S, Shin H, Jun Y, Eo T, Kim S, Kim T, Hwang D, Putzky P, Karkalousos D, Teuwen J, Miriakov N, Bakker B, Caan M, Welling M, Muckley MJ, Knoll F (2021)
Publication Type: Conference contribution
Publication year: 2021
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 12964 LNCS
Pages Range: 25-34
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Virtual, Online
ISBN: 9783030885519
DOI: 10.1007/978-3-030-88552-6_3
Abstract
The 2019 fastMRI challenge was an open challenge designed to advance research in the field of machine learning for MR image reconstruction. The goal for the participants was to reconstruct undersampled MRI k-space data. The original challenge left an open question as to how well the reconstruction methods will perform in the setting where there is a systematic difference between training and test data. In this work we tested the generalization performance of the submissions with respect to various perturbations, and despite differences in model architecture and training, all of the methods perform very similarly.
Authors with CRIS profile
Involved external institutions
NYU Langone Medical Center
United States (USA) (US)
AIRS Medical Inc.
Korea, Republic of (KR)
Imperial College London / The Imperial College of Science, Technology and Medicine
United Kingdom (GB)
Hyperfine Research Inc.
United States (USA) (US)
Philips Electronics Nederland BV
Netherlands (NL)
Philips Medical Systems MR Finland
Finland (FI)
Leiden University
Netherlands (NL)
Johns Hopkins University (JHU)
United States (USA) (US)
Yonsei University
Korea, Republic of (KR)
University of Amsterdam
Netherlands (NL)
Radboud University Nijmegen
Netherlands (NL)
Facebook, Inc.
United States (USA) (US)
United States (USA) (US)
AIRS Medical Inc.
Korea, Republic of (KR)
Imperial College London / The Imperial College of Science, Technology and Medicine
United Kingdom (GB)
Hyperfine Research Inc.
United States (USA) (US)
Philips Electronics Nederland BV
Netherlands (NL)
Philips Medical Systems MR Finland
Finland (FI)
Leiden University
Netherlands (NL)
Johns Hopkins University (JHU)
United States (USA) (US)
Yonsei University
Korea, Republic of (KR)
University of Amsterdam
Netherlands (NL)
Radboud University Nijmegen
Netherlands (NL)
Facebook, Inc.
United States (USA) (US)
How to cite
APA:
Johnson, P.M., Jeong, G., Hammernik, K., Schlemper, J., Qin, C., Duan, J.,... Knoll, F. (2021). Evaluation of the Robustness of Learned MR Image Reconstruction to Systematic Deviations Between Training and Test Data for the Models from the fastMRI Challenge. In Nandinee Haq, Patricia Johnson, Andreas Maier, Tobias Würfl, Jaejun Yoo (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 25-34). Virtual, Online: Springer Science and Business Media Deutschland GmbH.
MLA:
Johnson, Patricia M., et al. "Evaluation of the Robustness of Learned MR Image Reconstruction to Systematic Deviations Between Training and Test Data for the Models from the fastMRI Challenge." Proceedings of the 4th International Workshop on Machine Learning for Medical Image Reconstruction, MLMIR 2021 held in Conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021, Virtual, Online Ed. Nandinee Haq, Patricia Johnson, Andreas Maier, Tobias Würfl, Jaejun Yoo, Springer Science and Business Media Deutschland GmbH, 2021. 25-34.
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