Evaluation of the Robustness of Learned MR Image Reconstruction to Systematic Deviations Between Training and Test Data for the Models from the fastMRI Challenge

Autor: Johnson, Patricia M., Jeong, Geunu, Hammernik, Kerstin, Schlemper, Jo, Qin, Chen, Duan, Jinming, Rueckert, Daniel, Lee, Jingu, Pezzotti, Nicola, de Weerdt, Elwin, Yousefi, Sahar, Elmahdy, Mohamed S., van Gemert, Jeroen Hendrikus Franciscus, Schülke, Christophe, Doneva, Mariya, Nielsen, Tim, Kastryulin, Sergey, Lelieveldt, Boudewijn P. F., van Osch, Matthias J. P., Staring, Marius, Chen, Eric Z., Wang, Puyang, Chen, Xiao, Chen, Terrence, Patel, Vishal M., Sun, Shanhui, Shin, Hyungseob, Jun, Yohan, Eo, Taejoon, Kim, Sewon, Kim, Taeseong, Hwang, Dosik, Putzky, Patrick, Karkalousos, Dimitrios, Teuwen, Jonas, Miriakov, Nikita, Bakker, Bart, Caan, Matthan, Welling, Max, Muckley, Matthew J., Knoll, Florian, Haq, Nandinee, Johnson, Patricia, Maier, Andreas, Würfl, Tobias, Yoo, Jaejun

Publikováno v: Machine Learning for Medical Image Reconstruction ISBN: 9783030885519
MLMIR@MICCAI
Machine Learning for Medical Image Reconstruction-4th International Workshop, MLMIR 2021, Held in Conjunction with MICCAI 2021, Proceedings, 12964 LNCS, 25-34

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

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5b26c80b98598516c3dca1d580f44a20
https://doi.org/10.1007/978-3-030-88552-6_3