On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: Chronicles of the MEMENTO challenge
De Luca, Alberto; Ianus, Andrada; Leemans, Alexander; Palombo, Marco; Shemesh, Noam; Zhang, Hui; Alexander, Daniel C; Nilsson, Markus; Froeling, Martijn; Biessels, Geert-Jan; Zucchelli, Mauro; Frigo, Matteo; Albay, Enes; Sedlar, Sara; Alimi, Abib; Deslauriers-Gauthier, Samuel; Deriche, Rachid; Fick, Rutger; Afzali, Maryam; Pieciak, Tomasz; Bogusz, Fabian; Aja-Fernández, Santiago; Özarslan, Evren; Jones, Derek K; Chen, Haoze; Jin, Mingwu; Zhang, Zhijie; Wang, Fengxiang; Nath, Vishwesh; Parvathaneni, Prasanna; Morez, Jan; Sijbers, Jan; Jeurissen, Ben; Fadnavis, Shreyas; Endres, Stefan; Rokem, Ariel; Garyfallidis, Eleftherios; Sanchez, Irina; Prchkovska, Vesna; Rodrigues, Paulo; Landman, Bennet A; Schilling, Kurt G
(2021) NeuroImage, volume 240
(Article)
Abstract
Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and
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decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. The received submissions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings.
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Keywords: Neurology, Cognitive Neuroscience, Journal Article
ISSN: 1053-8119
Publisher: Academic Press Inc.
Note: Funding Information: Evren Özarslan is financially supported by Linköping University (LiU) Center for Industrial Information Technology (CENIIT), LiU Cancer, VINNOVA/ITEA3 17021 IMPACT, Analytic Imaging Diagnostic Arena (AIDA), and the Swedish Foundation for Strategic Research (RMX18-0056). Funding Information: Maryam Afzali and Derek K Jones are supported by a Wellcome Trust Investigator Award (096646/Z/11/Z) and DKJ by a Wellcome Trust Strategic Award (104943/Z/14/Z). Funding Information: The work by Inria co-authors was partially funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (ERC Advanced Grant agreement No 694665: CoBCoM - Computational Brain Connectivity Mapping, P.I. Rachi Deriche) and by the French government, through the 3IA C?te D'Azur Investments in the Future project managed by the National Research Agency (ANR) with the reference number ANR-19-P3IA-0002. Marco Palombo, Daniel C Alexander and Hui Zhang were supported by the EPSRC grant EP/N018702/1 and Marco Palombo by the UKRI Future Leaders Fellowship MR/T020296/1. Jan Morez is supported by a grant (ISLRA?2009) from the European Space Agency, by Belgian Science Policy Office?Prodex. Ben Jeurissen and Jan Sijbers received funding from the Research Foundation Flanders (FWO Vlaanderen: 12M3119N; G0D7216N). Maryam Afzali and Derek K Jones are supported by a Wellcome Trust Investigator Award (096646/Z/11/Z) and DKJ by a Wellcome Trust Strategic Award (104943/Z/14/Z). Tomasz Pieciak acknowledges the Polish National Agency for Academic Exchange for grant PN/BEK/2019/1/00421 under the Bekker programme and the Ministry of Science and Higher Education (Poland) under the scholarship for outstanding young scientists (692/STYP/13/2018). Fabian Bogusz acknowledges AGH Science and Technology, Poland (16.16.120.773). Evren ?zarslan is financially supported by Link?ping University (LiU) Center for Industrial Information Technology (CENIIT), LiU Cancer, VINNOVA/ITEA3 17021 IMPACT, Analytic Imaging Diagnostic Arena (AIDA), and the Swedish Foundation for Strategic Research (RMX18-0056). Andrada Ianus? work received the support of a fellowship from ?la Caixa? Foundation (ID 100010434) and from the European Union's Horizon 2020 research and innovation programme under the Marie Sk?odowska-Curie grant agreement No 847648, fellowship code LCF/BQ/PI20/11760029. Santiago Aja-Fernandez acknowledges the ?Ministerio de Ciencia e Innovaci?n? of Spain for research grant RTI2018-094569-B-I00. The work of Ariel Rokem and Eleftherios Garyfallidis is supported by grant 5R01EB027585-02 from the National Institute for Biomedical Imaging and Bioengineerring (PI: Eleftherios Garyfallidis). Funding Information: Marco Palombo, Daniel C Alexander and Hui Zhang were supported by the EPSRC grant EP/N018702/1 and Marco Palombo by the UKRI Future Leaders Fellowship MR/T020296/1. Funding Information: Tomasz Pieciak acknowledges the Polish National Agency for Academic Exchange for grant PN/BEK/2019/1/00421 under the Bekker programme and the Ministry of Science and Higher Education (Poland) under the scholarship for outstanding young scientists (692/STYP/13/2018). Funding Information: Jan Morez is supported by a grant (ISLRA‐2009) from the European Space Agency, by Belgian Science Policy Office‐Prodex. Ben Jeurissen and Jan Sijbers received funding from the Research Foundation Flanders (FWO Vlaanderen: 12M3119N; G0D7216N). Funding Information: Santiago Aja-Fernandez acknowledges the “Ministerio de Ciencia e Innovación” of Spain for research grant RTI2018-094569-B-I00. The work of Ariel Rokem and Eleftherios Garyfallidis is supported by grant 5R01EB027585-02 from the National Institute for Biomedical Imaging and Bioengineerring (PI: Eleftherios Garyfallidis). Funding Information: The work by Inria co-authors was partially funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation program (ERC Advanced Grant agreement No 694665: CoBCoM - Computational Brain Connectivity Mapping, P.I. Rachi Deriche) and by the French government, through the 3IA Côte D'Azur Investments in the Future project managed by the National Research Agency (ANR) with the reference number ANR-19-P3IA-0002. Funding Information: Andrada Ianus’ work received the support of a fellowship from ”la Caixa” Foundation (ID 100010434) and from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 847648, fellowship code LCF/BQ/PI20/11760029. Publisher Copyright: © 2021
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