TY - JOUR
T1 - Cross-scanner and cross-protocol diffusion MRI data harmonisation
T2 - A benchmark database and evaluation of algorithms
AU - Tax, Chantal Mw
AU - Grussu, Francesco
AU - Kaden, Enrico
AU - Ning, Lipeng
AU - Rudrapatna, Umesh
AU - Evans, John
AU - St-Jean, Samuel
AU - Leemans, Alexander
AU - Koppers, Simon
AU - Merhof, Dorit
AU - Ghosh, Aurobrata
AU - Tanno, Ryutaro
AU - Alexander, Daniel C
AU - Zappalà, Stefano
AU - Charron, Cyril
AU - Kusmia, Slawomir
AU - Linden, David Ej
AU - Jones, Derek K
AU - Veraart, Jelle
N1 - Funding Information:
CMWT is supported by a Rubicon grant (680-50-1527) from the Netherlands Organisation for Scientific Research and Wellcome Trust grant (096646/Z/11/Z). This project has received funding under the European Union's Horizon 2020 research and innovation programme under grant agreement No. 634541 and from Engineering and Physical Sciences Research Council (EPSRC EP/R006032/1, M020533/1), funding FG. SSJ is supported by the Fonds de Recherche du Québec - Nature et Technologies (Dossier 192865). AL and SSJ are supported by VIDI Grant 639.072.411 from the Netherlands Organisation for Scientific Research. RT acknowledges funding from Microsoft Research. DCA and AG acknowledge funding from EPSRC grants N018702 M020533 L022680. DEJL and DKJ were supported by MRC grant MR/K004360/1. Scan costs were supported by the National Centre for Mental Health (NCMH) with funds from Health and Care Support Wales and by the Wellcome Trust. JV is a Postdoctoral Fellow of the Research Foundation - Flanders (FWO; grant number 12S1615N). LN is supported in part by NIH grants R21MH115280 and R21MH116352.
Funding Information:
CMWT is supported by a Rubicon grant ( 680-50-1527 ) from the Netherlands Organisation for Scientific Research and Wellcome Trust grant ( 096646/Z/11/Z ). This project has received funding under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 634541 and from Engineering and Physical Sciences Research Council ( EPSRC EP/R006032/1 , M020533/1 ), funding FG. SSJ is supported by the Fonds de Recherche du Québec - Nature et Technologies (Dossier 192865 ). AL and SSJ are supported by VIDI Grant 639.072.411 from the Netherlands Organisation for Scientific Research . RT acknowledges funding from Microsoft Research . DCA and AG acknowledge funding from EPSRC grants N018702 M020533 L022680 . DEJL and DKJ were supported by MRC grant MR/K004360/1 . Scan costs were supported by the National Centre for Mental Health (NCMH) with funds from Health and Care Support Wales and by the Wellcome Trust . JV is a Postdoctoral Fellow of the Research Foundation - Flanders (FWO ; grant number 12S1615N ). LN is supported in part by NIH grants R21MH115280 and R21MH116352 .
Publisher Copyright:
© 2019
PY - 2019/7/15
Y1 - 2019/7/15
N2 - Diffusion MRI is being used increasingly in studies of the brain and other parts of the body for its ability to provide quantitative measures that are sensitive to changes in tissue microstructure. However, inter-scanner and inter-protocol differences are known to induce significant measurement variability, which in turn jeopardises the ability to obtain 'truly quantitative measures' and challenges the reliable combination of different datasets. Combining datasets from different scanners and/or acquired at different time points could dramatically increase the statistical power of clinical studies, and facilitate multi-centre research. Even though careful harmonisation of acquisition parameters can reduce variability, inter-protocol differences become almost inevitable with improvements in hardware and sequence design over time, even within a site. In this work, we present a benchmark diffusion MRI database of the same subjects acquired on three distinct scanners with different maximum gradient strength (40, 80, and 300 mT/m), and with 'standard' and 'state-of-the-art' protocols, where the latter have higher spatial and angular resolution. The dataset serves as a useful testbed for method development in cross-scanner/cross-protocol diffusion MRI harmonisation and quality enhancement. Using the database, we compare the performance of five different methods for estimating mappings between the scanners and protocols. The results show that cross-scanner harmonisation of single-shell diffusion data sets can reduce the variability between scanners, and highlight the promises and shortcomings of today's data harmonisation techniques.
AB - Diffusion MRI is being used increasingly in studies of the brain and other parts of the body for its ability to provide quantitative measures that are sensitive to changes in tissue microstructure. However, inter-scanner and inter-protocol differences are known to induce significant measurement variability, which in turn jeopardises the ability to obtain 'truly quantitative measures' and challenges the reliable combination of different datasets. Combining datasets from different scanners and/or acquired at different time points could dramatically increase the statistical power of clinical studies, and facilitate multi-centre research. Even though careful harmonisation of acquisition parameters can reduce variability, inter-protocol differences become almost inevitable with improvements in hardware and sequence design over time, even within a site. In this work, we present a benchmark diffusion MRI database of the same subjects acquired on three distinct scanners with different maximum gradient strength (40, 80, and 300 mT/m), and with 'standard' and 'state-of-the-art' protocols, where the latter have higher spatial and angular resolution. The dataset serves as a useful testbed for method development in cross-scanner/cross-protocol diffusion MRI harmonisation and quality enhancement. Using the database, we compare the performance of five different methods for estimating mappings between the scanners and protocols. The results show that cross-scanner harmonisation of single-shell diffusion data sets can reduce the variability between scanners, and highlight the promises and shortcomings of today's data harmonisation techniques.
UR - http://www.scopus.com/inward/record.url?scp=85064073375&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2019.01.077
DO - 10.1016/j.neuroimage.2019.01.077
M3 - Article
C2 - 30716459
SN - 1053-8119
VL - 195
SP - 285
EP - 299
JO - NeuroImage
JF - NeuroImage
ER -