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

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

De Luca, Alberto (author): Univ Med Ctr Utrecht, Netherlands; Univ Med Ctr Utrecht, Netherlands,University Medical Center Utrecht

Ianus, Andrada (author): Champalimaud Ctr Unknown, Portugal

Leemans, Alexander (author): Univ Med Ctr Utrecht, Netherlands,University Medical Center Utrecht

Palombo, Marco (author): UCL, England,University College London

Shemesh, Noam (author): Champalimaud Ctr Unknown, Portugal

Zhang, Hui (author): UCL, England,University College London

Alexander, Daniel C. (author): UCL, England,University College London

Nilsson, Markus (author): Lund University,Lunds universitet,Diagnostisk radiologi, Lund,Sektion V,Institutionen för kliniska vetenskaper, Lund,Medicinska fakulteten,LUCC: Lunds universitets cancercentrum,Övriga starka forskningsmiljöer,Diagnostic Radiology, (Lund),Section V,Department of Clinical Sciences, Lund,Faculty of Medicine,LUCC: Lund University Cancer Centre,Other Strong Research Environments

Froeling, Martijn (author): Univ Med Ctr Utrecht, Netherlands,University Medical Center Utrecht

Biessels, Geert-Jan (author): Univ Med Ctr Utrecht, Netherlands,University Medical Center Utrecht

Zucchelli, Mauro (author): Univ Cote dAzur, France,University of Côte d'Azur

Frigo, Matteo (author): Univ Cote dAzur, France,University of Côte d'Azur

Albay, Enes (author): Univ Cote dAzur, France; Istanbul Tech Univ, Turkey,University of Côte d'Azur,Istanbul Technical University

Sedlar, Sara (author): Univ Cote dAzur, France,University of Côte d'Azur

Alimi, Abib (author): Univ Cote dAzur, France,University of Côte d'Azur

Deslauriers-Gauthier, Samuel (author): Univ Cote dAzur, France,University of Côte d'Azur

Deriche, Rachid (author): Univ Cote dAzur, France,University of Côte d'Azur

Fick, Rutger (author): TRIBVN Healthcare, France

Afzali, Maryam (author): Cardiff Univ, Wales

Pieciak, Tomasz (author): AGH Univ Sci & Technol, Poland; Univ Valladolid, Spain,University of Valladolid,AGH University of Science and Technology

Bogusz, Fabian (author): AGH Univ Sci & Technol, Poland,AGH University of Science and Technology

Aja-Fernandez, Santiago (author): Univ Valladolid, Spain,University of Valladolid

Özarslan, Evren (author): Linköping University,Linköpings universitet,Avdelningen för medicinsk teknik,Tekniska fakulteten,Centrum för medicinsk bildvetenskap och visualisering, CMIV

Jones, Derek K. (author): Cardiff Univ, Wales

Chen, Haoze (author): North Univ China, Peoples R China

Jin, Mingwu (author): Univ Texas Arlington, TX 76019 USA,University of Texas at Arlington

Zhang, Zhijie (author): North Univ China, Peoples R China

Wang, Fengxiang (author): North Univ China, Peoples R China

Nath, Vishwesh (author): NVIDIA Corp, MD USA

Parvathaneni, Prasanna (author): NIH, MD 20892 USA,National Institutes of Health, United States

Morez, Jan (author): Univ Antwerp, Belgium,University of Antwerp

Sijbers, Jan (author): Univ Antwerp, Belgium,University of Antwerp

Jeurissen, Ben (author): Univ Antwerp, Belgium,University of Antwerp

Fadnavis, Shreyas (author): Indiana Univ, IN 47405 USA,Indiana University

Endres, Stefan (author): Univ Bremen, Germany,University of Bremen

Rokem, Ariel (author): Univ Washington, WA 98195 USA; Univ Washington, WA 98195 USA,University of Washington

Garyfallidis, Eleftherios (author): Indiana Univ, IN 47405 USA,Indiana University

Sanchez, Irina (author): QMENTA Inc, MA USA

Prchkovska, Vesna (author): QMENTA Inc, MA USA

Rodrigues, Paulo (author): QMENTA Inc, MA USA

Landman, Bennet A. (author): Vanderbilt Univ, TN 37235 USA

Schilling, Kurt G. (author): Vanderbilt Univ, TN 37235 USA; Vanderbilt Univ, TN 37232 USA,Vanderbilt University

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Elsevier BV, 2021
2021
English.
In: NeuroImage. - : Elsevier BV. - 1053-8119 .- 1095-9572. ; 240

Related links:: https://liu.diva-por... (primary) (Raw object); show more...; https://doi.org/10.1...; http://dx.doi.org/10... (free); https://urn.kb.se/re...; https://doi.org/10.1...; https://lup.lub.lu.s...; show less...

Journal article (peer-reviewed)

Abstract Subject headings

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.

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NATURAL SCIENCES: NATURAL SCIENCES; and Computer and Inf ...; and Bioinformatics

MEDICAL AND HEALTH SCIENCES: MEDICAL AND HEAL ...; and Clinical Medicin ...; and Radiology Nuclea ...

Articles in the publication: NeuroImage

By the university: Linköping University; Lund University

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

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