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On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge

View ORCID ProfileAlberto De Luca, View ORCID ProfileAndrada Ianus, View ORCID ProfileAlexander Leemans, View ORCID ProfileMarco Palombo, View ORCID ProfileNoam Shemesh, Hui Zhang, View ORCID ProfileDaniel C Alexander, View ORCID ProfileMarkus Nilsson, View ORCID ProfileMartijn Froeling, View ORCID ProfileGeert-Jan Biessels, View ORCID ProfileMauro Zucchelli, View ORCID ProfileMatteo Frigo, View ORCID ProfileEnes Albay, Sara Sedlar, Abib Alimi, View ORCID ProfileSamuel Deslauriers-Gauthier, View ORCID ProfileRachid Deriche, Rutger Fick, View ORCID ProfileMaryam Afzali, View ORCID ProfileTomasz Pieciak, Fabian Bogusz, View ORCID ProfileSantiago Aja-Fernández, View ORCID ProfileEvren Özarslan, Derek K Jones, Haoze Chen, Mingwu Jin, Zhijie Zhang, Fengxiang Wang, Vishwesh Nath, Prasanna Parvathaneni, View ORCID ProfileJan Morez, View ORCID ProfileJan Sijbers, View ORCID ProfileBen Jeurissen, Shreyas Fadnavis, View ORCID ProfileStefan Endres, View ORCID ProfileAriel Rokem, View ORCID ProfileEleftherios Garyfallidis, Irina Sanchez, Vesna Prchkovska, Paulo Rodrigues, Bennet A Landman, View ORCID ProfileKurt G Schilling
doi: https://doi.org/10.1101/2021.03.02.433228
Alberto De Luca
1PROVIDI Lab, Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands
2Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
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  • For correspondence: a.deluca-2@umcutrecht.nl
Andrada Ianus
3Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
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Alexander Leemans
1PROVIDI Lab, Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands
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Marco Palombo
4Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
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Noam Shemesh
3Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
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Hui Zhang
4Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
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Daniel C Alexander
4Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
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Markus Nilsson
5Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
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Martijn Froeling
6Department of Radiology, University Medical Center Utrecht
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Geert-Jan Biessels
2Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
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Mauro Zucchelli
7Inria Sophia Antipolis – Méditerranée, Université Côte d’Azur, Sophia Antipolis, France
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Matteo Frigo
7Inria Sophia Antipolis – Méditerranée, Université Côte d’Azur, Sophia Antipolis, France
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Enes Albay
7Inria Sophia Antipolis – Méditerranée, Université Côte d’Azur, Sophia Antipolis, France
8Istanbul Technical University, Istanbul, Turkey
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Sara Sedlar
7Inria Sophia Antipolis – Méditerranée, Université Côte d’Azur, Sophia Antipolis, France
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Abib Alimi
7Inria Sophia Antipolis – Méditerranée, Université Côte d’Azur, Sophia Antipolis, France
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Samuel Deslauriers-Gauthier
7Inria Sophia Antipolis – Méditerranée, Université Côte d’Azur, Sophia Antipolis, France
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Rachid Deriche
7Inria Sophia Antipolis – Méditerranée, Université Côte d’Azur, Sophia Antipolis, France
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Rutger Fick
9TRIBVN Healthcare, Paris, France
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Maryam Afzali
10Cardiff University Brain Research, Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
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Tomasz Pieciak
11AGH University of Science and Technology, Kraków, Poland
12LPI, ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain
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Fabian Bogusz
11AGH University of Science and Technology, Kraków, Poland
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Santiago Aja-Fernández
12LPI, ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain
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Evren Özarslan
13Department of Biomedical Engineering, Linköping University, Linköping, Sweden
14Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
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Derek K Jones
10Cardiff University Brain Research, Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
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Haoze Chen
15School of Instruments and Electronics, North University of China, Taiyuan, China
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Mingwu Jin
16Department of Physics, University of Texas at Arlington, Arlington, USA
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Zhijie Zhang
15School of Instruments and Electronics, North University of China, Taiyuan, China
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Fengxiang Wang
15School of Instruments and Electronics, North University of China, Taiyuan, China
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Vishwesh Nath
17NVIDIA Corporation, Bethesda, USA
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Prasanna Parvathaneni
18National Institute of Health, Bethesda, USA
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Jan Morez
19Imec-Vision lab, Department of Physics, University of Antwerp, Antwerp, Belgium
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Jan Sijbers
19Imec-Vision lab, Department of Physics, University of Antwerp, Antwerp, Belgium
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Ben Jeurissen
19Imec-Vision lab, Department of Physics, University of Antwerp, Antwerp, Belgium
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Shreyas Fadnavis
20Intelligent Systems Engineering, Indiana University Bloomington, Indiana, USA
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Stefan Endres
21Leibniz Institute for Materials Engineering — IWT, Faculty of Production Engineering, University of Bremen, Bremen, Germany
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Ariel Rokem
22Department of Psychology and the eScience Institute, University of Washington, Seattle, WA USA
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Eleftherios Garyfallidis
20Intelligent Systems Engineering, Indiana University Bloomington, Indiana, USA
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Irina Sanchez
23QMENTA Inc, Barcelona, Spain
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Vesna Prchkovska
23QMENTA Inc, Barcelona, Spain
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Paulo Rodrigues
23QMENTA Inc, Barcelona, Spain
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Bennet A Landman
24Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, USA
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Kurt G Schilling
24Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, USA
25Department of Radiology and Radiological Science, Vanderbilt University Medical Center, Nashville, USA
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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 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. Most predictions 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.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge
Alberto De Luca, Andrada Ianus, Alexander Leemans, Marco Palombo, Noam Shemesh, Hui Zhang, Daniel C Alexander, Markus Nilsson, Martijn Froeling, Geert-Jan Biessels, Mauro Zucchelli, Matteo Frigo, Enes Albay, Sara Sedlar, Abib Alimi, Samuel Deslauriers-Gauthier, Rachid Deriche, Rutger Fick, Maryam Afzali, Tomasz Pieciak, Fabian Bogusz, Santiago Aja-Fernández, Evren Özarslan, Derek K Jones, Haoze Chen, Mingwu Jin, Zhijie Zhang, Fengxiang Wang, Vishwesh Nath, Prasanna Parvathaneni, Jan Morez, Jan Sijbers, Ben Jeurissen, Shreyas Fadnavis, Stefan Endres, Ariel Rokem, Eleftherios Garyfallidis, Irina Sanchez, Vesna Prchkovska, Paulo Rodrigues, Bennet A Landman, Kurt G Schilling
bioRxiv 2021.03.02.433228; doi: https://doi.org/10.1101/2021.03.02.433228
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On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: chronicles of the MEMENTO challenge
Alberto De Luca, Andrada Ianus, Alexander Leemans, Marco Palombo, Noam Shemesh, Hui Zhang, Daniel C Alexander, Markus Nilsson, Martijn Froeling, Geert-Jan Biessels, Mauro Zucchelli, Matteo Frigo, Enes Albay, Sara Sedlar, Abib Alimi, Samuel Deslauriers-Gauthier, Rachid Deriche, Rutger Fick, Maryam Afzali, Tomasz Pieciak, Fabian Bogusz, Santiago Aja-Fernández, Evren Özarslan, Derek K Jones, Haoze Chen, Mingwu Jin, Zhijie Zhang, Fengxiang Wang, Vishwesh Nath, Prasanna Parvathaneni, Jan Morez, Jan Sijbers, Ben Jeurissen, Shreyas Fadnavis, Stefan Endres, Ariel Rokem, Eleftherios Garyfallidis, Irina Sanchez, Vesna Prchkovska, Paulo Rodrigues, Bennet A Landman, Kurt G Schilling
bioRxiv 2021.03.02.433228; doi: https://doi.org/10.1101/2021.03.02.433228

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