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
Affiliations
Mahbaneh Eshaghzadeh Torbati
Intelligent System Program, University of Pittsburgh School of Computing and Information, Pittsburgh, PA 15213, USA
Davneet S. Minhas
Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
Ghasan Ahmad
Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA
Erin E. O’Connor
Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA
John Muschelli
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
Charles M. Laymon
Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA
Zixi Yang
Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA
Ann D. Cohen
Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
Howard J. Aizenstein
Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
William E. Klunk
Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
Bradley T. Christian
Department of Medical Physics, University of Wisconsin–Madison, Madison, WI 53705, USA
Seong Jae Hwang
Intelligent System Program, University of Pittsburgh School of Computing and Information, Pittsburgh, PA 15213, USA; Department of Computer Science, University of Pittsburgh School of Computing and Information, Pittsburgh, PA 15213, USA
Ciprian M. Crainiceanu
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
Dana L. Tudorascu
Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA; Corresponding author.
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.
