A multi-scanner neuroimaging data harmonization using RAVEL and ComBat.
Alzheimer's disease
Harmonization
MRI
Normalization
Scanner effects
Journal
NeuroImage
ISSN: 1095-9572
Titre abrégé: Neuroimage
Pays: United States
ID NLM: 9215515
Informations de publication
Date de publication:
15 12 2021
15 12 2021
Historique:
received:
14
05
2021
revised:
07
10
2021
accepted:
28
10
2021
pubmed:
6
11
2021
medline:
11
2
2022
entrez:
5
11
2021
Statut:
ppublish
Résumé
Modern neuroimaging studies frequently combine data collected from multiple scanners and experimental conditions. Such data often contain substantial technical variability associated with image intensity scale (image intensity scales are not the same in different images) and scanner effects (images obtained from different scanners contain substantial technical biases). Here we evaluate and compare results of data analysis methods without any data transformation (RAW), with intensity normalization using RAVEL, with regional harmonization methods using ComBat, and a combination of RAVEL and ComBat. Methods are evaluated on a unique sample of 16 study participants who were scanned on both 1.5T and 3T scanners a few months apart. Neuroradiological evaluation was conducted for 7 different regions of interest (ROI's) pertinent to Alzheimer's disease (AD). Cortical measures and results indicate that: (1) RAVEL substantially improved the reproducibility of image intensities; (2) ComBat is preferred over RAVEL and the RAVEL-ComBat combination in terms of regional level harmonization due to more consistent harmonization across subjects and image-derived measures; (3) RAVEL and ComBat substantially reduced bias compared to analysis of RAW images, but RAVEL also resulted in larger variance; and (4) the larger root mean square deviation (RMSD) of RAVEL compared to ComBat is due mainly to its larger variance.
Identifiants
pubmed: 34736996
pii: S1053-8119(21)00964-2
doi: 10.1016/j.neuroimage.2021.118703
pmc: PMC8820090
mid: NIHMS1770946
pii:
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
118703Subventions
Organisme : NIA NIH HHS
ID : P30 AG066468
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG063752
Pays : United States
Organisme : NIA NIH HHS
ID : RF1 AG025516
Pays : United States
Informations de copyright
Copyright © 2021. Published by Elsevier Inc.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors declare that they have no competing interests.
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