A multi-scanner neuroimaging data harmonization using RAVEL and ComBat
AuthorEshaghzadeh Torbati, Mahbaneh
Minhas, Davneet S
O'Connor, Erin E
Laymon, Charles M
Cohen, Ann D
Aizenstein, Howard J
Klunk, William E
Christian, Bradley T
Hwang, Seong Jae
Crainiceanu, Ciprian M
Tudorascu, Dana L
MetadataShow full item record
AbstractModern 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.
Rights/TermsCopyright © 2021. Published by Elsevier Inc.
Identifier to cite or link to this itemhttp://hdl.handle.net/10713/17150
- Removing inter-subject technical variability in magnetic resonance imaging studies.
- Authors: Fortin JP, Sweeney EM, Muschelli J, Crainiceanu CM, Shinohara RT, Alzheimer's Disease Neuroimaging Initiative.
- Issue date: 2016 May 15
- Longitudinal ComBat: A method for harmonizing longitudinal multi-scanner imaging data.
- Authors: Beer JC, Tustison NJ, Cook PA, Davatzikos C, Sheline YI, Shinohara RT, Linn KA, Alzheimer’s Disease Neuroimaging Initiative.
- Issue date: 2020 Oct 15
- Comparison of traveling-subject and ComBat harmonization methods for assessing structural brain characteristics.
- Authors: Maikusa N, Zhu Y, Uematsu A, Yamashita A, Saotome K, Okada N, Kasai K, Okanoya K, Yamashita O, Tanaka SC, Koike S
- Issue date: 2021 Nov
- Intensity warping for multisite MRI harmonization.
- Authors: Wrobel J, Martin ML, Bakshi R, Calabresi PA, Elliot M, Roalf D, Gur RC, Gur RE, Henry RG, Nair G, Oh J, Papinutto N, Pelletier D, Reich DS, Rooney WD, Satterthwaite TD, Stern W, Prabhakaran K, Sicotte NL, Shinohara RT, Goldsmith J, NAIMS Cooperative.
- Issue date: 2020 Dec
- Impact of Preprocessing and Harmonization Methods on the Removal of Scanner Effects in Brain MRI Radiomic Features.
- Authors: Li Y, Ammari S, Balleyguier C, Lassau N, Chouzenoux E
- Issue date: 2021 Jun 15