Cross-scanner and cross-protocol multi-shell diffusion MRI data harmonization: Algorithms and results

Lipeng Ning (Corresponding author), Elisenda Bonet-Carne, Francesco Grussu, Farshid Sepehrband, Enrico Kaden, Jelle Veraart, Stefano B. Blumberg, Can Son Khoo, Marco Palombo, Iasonas Kokkinos, Daniel C. Alexander, Jaume Coll-Font, Benoit Scherrer, Simon K. Warfield, Suheyla Cetin Karayumak, Yogesh Rathi, Simon Koppers, Leon Weninger, Julia Ebert, Dorit MerhofDaniel Moyer, Maximilian Pietsch, Daan Christiaens, Rui Azeredo Gomes Teixeira, Jacques Donald Tournier, Kurt G. Schilling, Yuankai Huo, Vishwesh Nath, Colin Hansen, Justin Blaber, Bennett A. Landman, Andrey Zhylka, Josien P.W. Pluim, Greg Parker, Umesh Rudrapatna, John Evans, Cyril Charron, Derek K. Jones, Chantal M.W. Tax

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

45 Citaten (Scopus)

Samenvatting

Cross-scanner and cross-protocol variability of diffusion magnetic resonance imaging (dMRI) data are known to be major obstacles in multi-site clinical studies since they limit the ability to aggregate dMRI data and derived measures. Computational algorithms that harmonize the data and minimize such variability are critical to reliably combine datasets acquired from different scanners and/or protocols, thus improving the statistical power and sensitivity of multi-site studies. Different computational approaches have been proposed to harmonize diffusion MRI data or remove scanner-specific differences. To date, these methods have mostly been developed for or evaluated on single b-value diffusion MRI data. In this work, we present the evaluation results of 19 algorithms that are developed to harmonize the cross-scanner and cross-protocol variability of multi-shell diffusion MRI using a benchmark database. The proposed algorithms rely on various signal representation approaches and computational tools, such as rotational invariant spherical harmonics, deep neural networks and hybrid biophysical and statistical approaches. The benchmark database consists of data acquired from the same subjects on two scanners with different maximum gradient strength (80 and 300 ​mT/m) and with two protocols. We evaluated the performance of these algorithms for mapping multi-shell diffusion MRI data across scanners and across protocols using several state-of-the-art imaging measures. The results show that data harmonization algorithms can reduce the cross-scanner and cross-protocol variabilities to a similar level as scan-rescan variability using the same scanner and protocol. In particular, the LinearRISH algorithm based on adaptive linear mapping of rotational invariant spherical harmonics features yields the lowest variability for our data in predicting the fractional anisotropy (FA), mean diffusivity (MD), mean kurtosis (MK) and the rotationally invariant spherical harmonic (RISH) features. But other algorithms, such as DIAMOND, SHResNet, DIQT, CMResNet show further improvement in harmonizing the return-to-origin probability (RTOP). The performance of different approaches provides useful guidelines on data harmonization in future multi-site studies.

Originele taal-2Engels
Artikelnummer117128
Aantal pagina's16
TijdschriftNeuroimage
Volume221
DOI's
StatusGepubliceerd - 1 nov. 2020

Financiering

CMWT is supported by a Rubicon grant ( 680-50-1527 ) from the Netherlands Organisation for Scientific Research (NWO) 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. 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 , R21MH116352 and K01MH117346 . SCK and YR are supported in part by NIH grant R01MH119222 . AZ and JP have received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 765148 . KS is supported in part by NIH grants R01EB017230 , and T32EB001628 . FS is supported in part by USC ADRC 5P50AG005142 and R01NS100973 . EK acknowledges support from UK EPSRC EP/M020533/1 , EP/N018702/1 and EU H2020 634541 . DC is supported by the Flemish Research Foundation (FWO ; fellowship number 12ZV420N ). MP is funded by UKRI Future Leaders Fellowship MR/T020296/1 .

FinanciersFinanciernummer
European Union H2020
European Union’s Horizon Europe research and innovation programme
Health and Care Support Wales
H2020 Marie Skłodowska-Curie Actions
National Centre for Mental Health
Research Foundation Flanders
USC ADRC5P50AG005142, EP/M020533/1, EP/N018702/1
National Institutes of Health, NIHR01MH119222, R21MH115280
National Institute of Mental HealthR21MH116352, K01MH117346
National Institute of Neurological Disorders and StrokeR01NS100973
National Institute of Biomedical Imaging and BioengineeringT32EB001628, R01EB017230
Wellcome Trust096646/Z/11/Z
European Union’s Horizon Europe research and innovation programme634541
H2020 Marie Skłodowska-Curie Actions765148
UK Research and InnovationMR/T020296/1
Medical Research CouncilMR/K004360/1
Engineering and Physical Sciences Research CouncilM020533/1, EP/R006032/1
Fonds Wetenschappelijk Onderzoek12ZV420N, 12S1615N
Nederlandse Organisatie voor Wetenschappelijk Onderzoek
Mauritius Research Council
National Center for Mental Health

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