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WFRF:(Alexander Jan)
 

Sökning: WFRF:(Alexander Jan) > On the generalizabi...

LIBRIS Formathandbok  (Information om MARC21)
FältnamnIndikatorerMetadata
00010140naa a2200877 4500
001oai:DiVA.org:liu-179831
003SwePub
008211005s2021 | |||||||||||000 ||eng|
009oai:lup.lub.lu.se:3d9f4686-5cdd-423c-8ffc-b9f5eb692e4f
0247 a https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1798312 URI
0247 a https://doi.org/10.1016/j.neuroimage.2021.1183672 DOI
0247 a https://lup.lub.lu.se/record/3d9f4686-5cdd-423c-8ffc-b9f5eb692e4f2 URI
040 a (SwePub)liud (SwePub)lu
041 a engb eng
042 9 SwePub
072 7a ref2 swepub-contenttype
072 7a art2 swepub-publicationtype
1001 a De Luca, Albertou Univ Med Ctr Utrecht, Netherlands; Univ Med Ctr Utrecht, Netherlands,University Medical Center Utrecht4 aut
2451 0a On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types :b Chronicles of the MEMENTO challenge
264 1b Elsevier BV,c 2021
338 a electronic2 rdacarrier
500 a Funding Agencies|European Research Council (ERC) under the European UnionEuropean Research Council (ERC) [694665]; French government, through the 3IA Cote DAzur Investments in the Future project [ANR-19-P3IA-0002]; EPSRCUK Research & Innovation (UKRI)Engineering & Physical Sciences Research Council (EPSRC) [EP/N018702/1, MR/T020296/1, ISLRA-2009]; European Space AgencyEuropean Space AgencyEuropean Commission; Belgian Science Policy Office-ProdexBelgian Federal Science Policy Office; Research Foundation Flanders (FWO Vlaanderen)FWO [12M3119N, G0D7216N]; Wellcome Trust Investigator AwardWellcome Trust [096646/Z/11/Z]; Wellcome Trust Strategic AwardWellcome Trust [104943/Z/14/Z]; Polish National Agency for Academic ExchangePolish National Agency for Academic Exchange (NAWA) [PN/BEK/2019/1/00421]; Ministry of Science and Higher Education (Poland)Ministry of Science and Higher Education, Poland [692/STYP/13/2018]; AGH Science and Technology, Poland [16.16.120.773]; Linkoping University (LiU) Center for Industrial Information Technology (CENIIT); LiU Cancer [VINNOVA/ITEA3 17021 IMPACT]; Swedish Foundation for Strategic ResearchSwedish Foundation for Strategic Research [RMX18-0056]; "la Caixa" FoundationLa Caixa Foundation [100010434]; European UnionEuropean Commission [847648, LCF/BQ/PI20/11760029]; Ministerio de Ciencia e Innovacion" of SpainSpanish Government [RTI2018-094569-B-I00]; National Institute for Biomedical Imaging [5R01EB027585-02]
520 a 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.
650 7a NATURVETENSKAPx Data- och informationsvetenskapx Bioinformatik0 (SwePub)102032 hsv//swe
650 7a NATURAL SCIENCESx Computer and Information Sciencesx Bioinformatics0 (SwePub)102032 hsv//eng
650 7a MEDICIN OCH HÄLSOVETENSKAPx Klinisk medicinx Radiologi och bildbehandling0 (SwePub)302082 hsv//swe
650 7a MEDICAL AND HEALTH SCIENCESx Clinical Medicinex Radiology, Nuclear Medicine and Medical Imaging0 (SwePub)302082 hsv//eng
7001 a Ianus, Andradau Champalimaud Ctr Unknown, Portugal4 aut
7001 a Leemans, Alexanderu Univ Med Ctr Utrecht, Netherlands,University Medical Center Utrecht4 aut
7001 a Palombo, Marcou UCL, England,University College London4 aut
7001 a Shemesh, Noamu Champalimaud Ctr Unknown, Portugal4 aut
7001 a Zhang, Huiu UCL, England,University College London4 aut
7001 a Alexander, Daniel C.u UCL, England,University College London4 aut
7001 a Nilsson, Markusu 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 Environments4 aut0 (Swepub:lu)med-mun
7001 a Froeling, Martijnu Univ Med Ctr Utrecht, Netherlands,University Medical Center Utrecht4 aut
7001 a Biessels, Geert-Janu Univ Med Ctr Utrecht, Netherlands,University Medical Center Utrecht4 aut
7001 a Zucchelli, Maurou Univ Cote dAzur, France,University of Côte d'Azur4 aut
7001 a Frigo, Matteou Univ Cote dAzur, France,University of Côte d'Azur4 aut
7001 a Albay, Enesu Univ Cote dAzur, France; Istanbul Tech Univ, Turkey,University of Côte d'Azur,Istanbul Technical University4 aut
7001 a Sedlar, Sarau Univ Cote dAzur, France,University of Côte d'Azur4 aut
7001 a Alimi, Abibu Univ Cote dAzur, France,University of Côte d'Azur4 aut
7001 a Deslauriers-Gauthier, Samuelu Univ Cote dAzur, France,University of Côte d'Azur4 aut
7001 a Deriche, Rachidu Univ Cote dAzur, France,University of Côte d'Azur4 aut
7001 a Fick, Rutgeru TRIBVN Healthcare, France4 aut
7001 a Afzali, Maryamu Cardiff Univ, Wales4 aut
7001 a Pieciak, Tomaszu AGH Univ Sci & Technol, Poland; Univ Valladolid, Spain,University of Valladolid,AGH University of Science and Technology4 aut
7001 a Bogusz, Fabianu AGH Univ Sci & Technol, Poland,AGH University of Science and Technology4 aut
7001 a Aja-Fernandez, Santiagou Univ Valladolid, Spain,University of Valladolid4 aut
7001 a Özarslan, Evrenu Linköping University,Linköpings universitet,Avdelningen för medicinsk teknik,Tekniska fakulteten,Centrum för medicinsk bildvetenskap och visualisering, CMIV4 aut0 (Swepub:liu)evroz77
7001 a Jones, Derek K.u Cardiff Univ, Wales4 aut
7001 a Chen, Haozeu North Univ China, Peoples R China4 aut
7001 a Jin, Mingwuu Univ Texas Arlington, TX 76019 USA,University of Texas at Arlington4 aut
7001 a Zhang, Zhijieu North Univ China, Peoples R China4 aut
7001 a Wang, Fengxiangu North Univ China, Peoples R China4 aut
7001 a Nath, Vishweshu NVIDIA Corp, MD USA4 aut
7001 a Parvathaneni, Prasannau NIH, MD 20892 USA,National Institutes of Health, United States4 aut
7001 a Morez, Janu Univ Antwerp, Belgium,University of Antwerp4 aut
7001 a Sijbers, Janu Univ Antwerp, Belgium,University of Antwerp4 aut
7001 a Jeurissen, Benu Univ Antwerp, Belgium,University of Antwerp4 aut
7001 a Fadnavis, Shreyasu Indiana Univ, IN 47405 USA,Indiana University4 aut
7001 a Endres, Stefanu Univ Bremen, Germany,University of Bremen4 aut
7001 a Rokem, Arielu Univ Washington, WA 98195 USA; Univ Washington, WA 98195 USA,University of Washington4 aut
7001 a Garyfallidis, Eleftheriosu Indiana Univ, IN 47405 USA,Indiana University4 aut
7001 a Sanchez, Irinau QMENTA Inc, MA USA4 aut
7001 a Prchkovska, Vesnau QMENTA Inc, MA USA4 aut
7001 a Rodrigues, Paulou QMENTA Inc, MA USA4 aut
7001 a Landman, Bennet A.u Vanderbilt Univ, TN 37235 USA4 aut
7001 a Schilling, Kurt G.u Vanderbilt Univ, TN 37235 USA; Vanderbilt Univ, TN 37232 USA,Vanderbilt University4 aut
7102 a Univ Med Ctr Utrecht, Netherlands; Univ Med Ctr Utrecht, Netherlandsb University Medical Center Utrecht4 org
7730 t NeuroImaged : Elsevier BVg 240q 240x 1053-8119x 1095-9572
8564 u https://liu.diva-portal.org/smash/get/diva2:1600480/FULLTEXT01.pdfx primaryx Raw objecty fulltext:print
8564 u https://doi.org/10.1016/j.neuroimage.2021.118367
8564 u http://dx.doi.org/10.1016/j.neuroimage.2021.118367x freey FULLTEXT
8564 8u https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-179831
8564 8u https://doi.org/10.1016/j.neuroimage.2021.118367
8564 8u https://lup.lub.lu.se/record/3d9f4686-5cdd-423c-8ffc-b9f5eb692e4f

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