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 PieciakFabian 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

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

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. 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.

Original languageEnglish
Article number118367
JournalNeuroImage
Volume240
DOIs
Publication statusPublished - 15 Oct 2021
Externally publishedYes

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