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"Evaluation of the Robustness of Learned MR Image Reconstruction to Systematic Deviations Between Training and Test Data for the Models from the fastMRI Challenge"
Patricia M. Johnson, Geunu Jeong, Kerstin Hammernik, Jo Schlemper, Chen Qin, Jinming Duan, Daniel Rueckert, Jingu Lee, Nicola Pezzotti, Elwin De Weerdt, Sahar Yousefi, Mohamed S. Elmahdy, Jeroen Hendrikus Franciscus Van Gemert, Chistophe Schuelke, Mariya Doneva, Tim Nielsen, Sergey Kastryulin, Boudewijn P. F. Lelieveldt, Matthias J. P. Van Osch, Marius Staring, Eric Z. Chen, Puyang Wang, Xiao Chen, Terrence Chen, Vishal M. Patel, Shanhui Sun, Hyungseob Shin, Yohan Jun, Taejoon Eo, Sewon Kim, Taeseong Kim, Dosik Hwang, Patrick Putzky, Dimitrios Karkalousos, Jonas Teuwen, Nikita Miriakov, Bart Bakker, Matthan Caan, Max Welling, Matthew J. Muckley and Florian Knoll
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.
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Copyright © 2021 by the authors.
Published version © 2021 by Springer Lecture Notes in Computer Science.
Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.
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BibTeX entry
| @inproceedings{Johnson:2021, |
| author |
= {Johnson, Patricia M. and Jeong, Geunu and Hammernik, Kerstin and Schlemper, Jo and Qin, Chen and Duan, Jinming and Rueckert, Daniel and Lee, Jingu and Pezzotti, Nicola and De Weerdt, Elwin and Yousefi, Sahar and Elmahdy, Mohamed S. and Van Gemert, Jeroen Hendrikus Franciscus and Schuelke, Chistophe and Doneva, Mariya and Nielsen, Tim and Kastryulin, Sergey and Lelieveldt, Boudewijn P. F. and Van Osch, Matthias J. P. and Staring, Marius and Chen, Eric Z. and Wang, Puyang and Chen, Xiao and Chen, Terrence and Patel, Vishal M. and Sun, Shanhui and Shin, Hyungseob and Jun, Yohan and Eo, Taejoon and Kim, Sewon and Kim, Taeseong and Hwang, Dosik and Putzky, Patrick and Karkalousos, Dimitrios and Teuwen, Jonas and Miriakov, Nikita and Bakker, Bart and Caan, Matthan and Welling, Max and Muckley, Matthew J. and Knoll, Florian}, |
| title |
= {Evaluation of the Robustness of Learned MR Image Reconstruction to Systematic Deviations Between Training and Test Data for the Models from the fastMRI Challenge}, |
| booktitle |
= {Machine Learning for Medical Image Reconstruction, MICCAI workshop}, |
| editor |
= {Haq, Nandinee F. and Johnson, Patricia and Maier, Andreas and Würfl, Tobias and Yoo, Jaejun}, |
| address |
= {Strasbourg, France}, |
| series |
= {Lecture Notes in Computer Science}, |
| volume |
= {12964}, |
| pages |
= {25 - 34}, |
| month |
= {October}, |
| year |
= {2021}, |
| } |
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