A multi-scanner neuroimaging data harmonization using RAVEL and ComBat

Mahbaneh Eshaghzadeh Torbati, Davneet S. Minhas, Ghasan Ahmad, Erin E. O'Connor, John Muschelli, Charles M. Laymon, Zixi Yang, Ann D. Cohen, Howard J. Aizenstein, William E. Klunk, Bradley T. Christian, Seong Jae Hwang, Ciprian M. Crainiceanu, Dana L. Tudorascu

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1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Article number118703
JournalNeuroImage
Volume245
DOIs
Publication statusPublished - 2021 Dec 15

Bibliographical note

Funding Information:
This work was supported by the National Institutes of Health/NIA grants: R01 AG063752 (to D.L. Tudorascu) and RF1 AG025516 (to W.E. Klunk), in addition to the University of Pittsburgh ADRC Grant: P30 AG066468 (partial support for S.J. Hwang in form of an internal pilot). We thank Dr. James Wilson for the useful discussion.

Publisher Copyright:
© 2021

All Science Journal Classification (ASJC) codes

  • Neurology
  • Cognitive Neuroscience

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