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; Christophe  Schülke; 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; Florian  Knoll

doi:10.1007/978-3-030-88552-6_3

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, Christophe Schülke, Mariya Doneva, Tim Nielsen, Sergey Kastryulin, Boudewijn P. F. Lelieveldt, Matthias J. P. Van Osch, Marius StaringEric 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, Florian Knoll

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

3 Citaten (Scopus)

Samenvatting

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.

Originele taal-2	Engels
Titel	Machine Learning for Medical Image Reconstruction - 4th International Workshop, MLMIR 2021, Held in Conjunction with MICCAI 2021, Proceedings
Subtitel	4th International Workshop, MLMIR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings
Redacteuren	Nandinee Haq, Patricia Johnson, Andreas Maier, Tobias Würfl, Jaejun Yoo
Uitgeverij	Springer Nature
Pagina's	25-34
Aantal pagina's	10
ISBN van elektronische versie	978-3-030-88552-6
ISBN van geprinte versie	978-3-030-88551-9
DOI's	https://doi.org/10.1007/978-3-030-88552-6_3
Status	Gepubliceerd - 25 sep. 2021

Publicatie series

Naam	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12964 LNCS
ISSN van geprinte versie	0302-9743
ISSN van elektronische versie	1611-3349

Toegang tot document

10.1007/978-3-030-88552-6_3

Andere bestanden en links

Link naar publicatie in Scopus

Citeer dit

Johnson, P. M., Jeong, G., Hammernik, K., Schlemper, J., Qin, C., Duan, J., Rueckert, D., Lee, J., Pezzotti, N., De Weerdt, E., Yousefi, S., Elmahdy, M. S., Van Gemert, J. H. F., Schülke, C., Doneva, M., Nielsen, T., Kastryulin, S., Lelieveldt, B. P. F., Van Osch, M. J. P., ... 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 N. Haq, P. Johnson, A. Maier, T. Würfl, & J. Yoo (editors), Machine Learning for Medical Image Reconstruction - 4th International Workshop, MLMIR 2021, Held in Conjunction with MICCAI 2021, Proceedings: 4th International Workshop, MLMIR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings (blz. 25-34). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12964 LNCS). Springer Nature. https://doi.org/10.1007/978-3-030-88552-6_3

Johnson, Patricia M. ; Jeong, Geunu ; Hammernik, Kerstin 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. Machine Learning for Medical Image Reconstruction - 4th International Workshop, MLMIR 2021, Held in Conjunction with MICCAI 2021, Proceedings: 4th International Workshop, MLMIR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings. redacteur / Nandinee Haq ; Patricia Johnson ; Andreas Maier ; Tobias Würfl ; Jaejun Yoo. Springer Nature, 2021. blz. 25-34 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{c19a1349ddd04bfda57132e92e230680,

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",

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.",

author = "Johnson, {Patricia M.} and Geunu Jeong and Kerstin Hammernik and Jo Schlemper and Chen Qin and Jinming Duan and Daniel Rueckert and Jingu Lee and Nicola Pezzotti and {De Weerdt}, Elwin and Sahar Yousefi and Elmahdy, {Mohamed S.} and {Van Gemert}, {Jeroen Hendrikus Franciscus} and Christophe Sch{\"u}lke and Mariya Doneva and Tim Nielsen and Sergey Kastryulin and Lelieveldt, {Boudewijn P. F.} and {Van Osch}, {Matthias J. P.} and Marius Staring and Chen, {Eric Z.} and Puyang Wang and Xiao Chen and Terrence Chen and Patel, {Vishal M.} and Shanhui Sun and Hyungseob Shin and Yohan Jun and Taejoon Eo and Sewon Kim and Taeseong Kim and Dosik Hwang and Patrick Putzky and Dimitrios Karkalousos and Jonas Teuwen and Nikita Miriakov and Bart Bakker and Matthan Caan and Max Welling and Muckley, {Matthew J.} and Florian Knoll",

year = "2021",

month = sep,

day = "25",

doi = "10.1007/978-3-030-88552-6_3",

language = "English",

isbn = "978-3-030-88551-9",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Nature",

pages = "25--34",

editor = "Nandinee Haq and Patricia Johnson and Andreas Maier and Tobias W{\"u}rfl and Jaejun Yoo",

booktitle = "Machine Learning for Medical Image Reconstruction - 4th International Workshop, MLMIR 2021, Held in Conjunction with MICCAI 2021, Proceedings",

address = "Singapore",

}

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, Schülke, 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, 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 N Haq, P Johnson, A Maier, T Würfl & J Yoo (uitgave), Machine Learning for Medical Image Reconstruction - 4th International Workshop, MLMIR 2021, Held in Conjunction with MICCAI 2021, Proceedings: 4th International Workshop, MLMIR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12964 LNCS, Springer Nature, blz. 25-34. https://doi.org/10.1007/978-3-030-88552-6_3

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, Patricia M. ; Jeong, Geunu ; Hammernik, Kerstin et al.
Machine Learning for Medical Image Reconstruction - 4th International Workshop, MLMIR 2021, Held in Conjunction with MICCAI 2021, Proceedings: 4th International Workshop, MLMIR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings. uitgave / Nandinee Haq; Patricia Johnson; Andreas Maier; Tobias Würfl; Jaejun Yoo. Springer Nature, 2021. blz. 25-34 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12964 LNCS).

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

TY - GEN

T1 - Evaluation of the Robustness of Learned MR Image Reconstruction to Systematic Deviations Between Training and Test Data for the Models from the fastMRI Challenge

AU - Johnson, Patricia M.

AU - Jeong, Geunu

AU - Hammernik, Kerstin

AU - Schlemper, Jo

AU - Qin, Chen

AU - Duan, Jinming

AU - Rueckert, Daniel

AU - Lee, Jingu

AU - Pezzotti, Nicola

AU - De Weerdt, Elwin

AU - Yousefi, Sahar

AU - Elmahdy, Mohamed S.

AU - Van Gemert, Jeroen Hendrikus Franciscus

AU - Schülke, Christophe

AU - Doneva, Mariya

AU - Nielsen, Tim

AU - Kastryulin, Sergey

AU - Lelieveldt, Boudewijn P. F.

AU - Van Osch, Matthias J. P.

AU - Staring, Marius

AU - Chen, Eric Z.

AU - Wang, Puyang

AU - Chen, Xiao

AU - Chen, Terrence

AU - Patel, Vishal M.

AU - Sun, Shanhui

AU - Shin, Hyungseob

AU - Jun, Yohan

AU - Eo, Taejoon

AU - Kim, Sewon

AU - Kim, Taeseong

AU - Hwang, Dosik

AU - Putzky, Patrick

AU - Karkalousos, Dimitrios

AU - Teuwen, Jonas

AU - Miriakov, Nikita

AU - Bakker, Bart

AU - Caan, Matthan

AU - Welling, Max

AU - Muckley, Matthew J.

AU - Knoll, Florian

PY - 2021/9/25

Y1 - 2021/9/25

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85116879187&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-88552-6_3

DO - 10.1007/978-3-030-88552-6_3

M3 - Conference contribution

SN - 978-3-030-88551-9

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 25

EP - 34

BT - Machine Learning for Medical Image Reconstruction - 4th International Workshop, MLMIR 2021, Held in Conjunction with MICCAI 2021, Proceedings

A2 - Haq, Nandinee

A2 - Johnson, Patricia

A2 - Maier, Andreas

A2 - Würfl, Tobias

A2 - Yoo, Jaejun

PB - Springer Nature

ER -

Johnson PM, Jeong G, Hammernik K, Schlemper J, Qin C, Duan J 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. In Haq N, Johnson P, Maier A, Würfl T, Yoo J, redacteurs, Machine Learning for Medical Image Reconstruction - 4th International Workshop, MLMIR 2021, Held in Conjunction with MICCAI 2021, Proceedings: 4th International Workshop, MLMIR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, October 1, 2021, Proceedings. Springer Nature. 2021. blz. 25-34. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-88552-6_3

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

Samenvatting

Publicatie series

Toegang tot document

Andere bestanden en links

Vingerafdruk

Citeer dit