Corresponding author.; Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, USA
M.L. Martin
Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
R. Bakshi
Laboratory for Neuroimaging Research, Partners Multiple Sclerosis Center, Department of Neurology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA; Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
P.A. Calabresi
Department of Neurology, the Johns Hopkins University School of Medicine, Baltimore, MD, USA
M. Elliot
Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA
D. Roalf
Brain Behavior Laboratory, Department of Psychiatry, Neuropsychiatry Section, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
R.C. Gur
Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA; Brain Behavior Laboratory, Department of Psychiatry, Neuropsychiatry Section, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) at the University of Pennsylvania and Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
R.E. Gur
Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA; Brain Behavior Laboratory, Department of Psychiatry, Neuropsychiatry Section, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Lifespan Brain Institute (LiBI) at the University of Pennsylvania and Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
R.G. Henry
Department of Neurology, University of California - San Francisco, San Francisco, CA, USA
G. Nair
Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
J. Oh
Department of Neurology, the Johns Hopkins University School of Medicine, Baltimore, MD, USA; St. Michael’s Hospital, University of Toronto, Toronto, Ontario, Canada
N. Papinutto
Department of Neurology, University of California - San Francisco, San Francisco, CA, USA
D. Pelletier
Department of Neurology, University of California - San Francisco, San Francisco, CA, USA
D.S. Reich
Department of Neurology, the Johns Hopkins University School of Medicine, Baltimore, MD, USA; Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA
W.D. Rooney
Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR, USA
T.D. Satterthwaite
Brain Behavior Laboratory, Department of Psychiatry, Neuropsychiatry Section, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
W. Stern
Department of Neurology, University of California - San Francisco, San Francisco, CA, USA
K. Prabhakaran
Brain Behavior Laboratory, Department of Psychiatry, Neuropsychiatry Section, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
N.L. Sicotte
Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA
R.T. Shinohara
Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
J. Goldsmith
Department of Biostatistics, Mailman School of Public Health, Columbia University, USA
In multisite neuroimaging studies there is often unwanted technical variation across scanners and sites. These “scanner effects” can hinder detection of biological features of interest, produce inconsistent results, and lead to spurious associations. We propose mica (multisite image harmonization by cumulative distribution function alignment), a tool to harmonize images taken on different scanners by identifying and removing within-subject scanner effects. Our goals in the present study were to (1) establish a method that removes scanner effects by leveraging multiple scans collected on the same subject, and, building on this, (2) develop a technique to quantify scanner effects in large multisite studies so these can be reduced as a preprocessing step. We illustrate scanner effects in a brain MRI study in which the same subject was measured twice on seven scanners, and assess our method’s performance in a second study in which ten subjects were scanned on two machines. We found that unharmonized images were highly variable across site and scanner type, and our method effectively removed this variability by aligning intensity distributions. We further studied the ability to predict image harmonization results for a scan taken on an existing subject at a new site using cross-validation.
