Intensity warping for multisite MRI harmonization

J Wrobel; M L Martin; R Bakshi; P A Calabresi; M Elliot; D Roalf; R C Gur; R E Gur; R G Henry; G Nair; J Oh; N Papinutto; D Pelletier; D S Reich; W D Rooney; T D Satterthwaite; W Stern; K Prabhakaran; N L Sicotte; R T Shinohara; J Goldsmith; NAIMS Cooperative

doi:10.1016/j.neuroimage.2020.117242

Intensity warping for multisite MRI harmonization

Neuroimage. 2020 Dec:223:117242. doi: 10.1016/j.neuroimage.2020.117242. Epub 2020 Aug 14.

Authors

J Wrobel¹, M L Martin², R Bakshi³, P A Calabresi⁴, M Elliot⁵, D Roalf⁶, R C Gur⁷, R E Gur⁷, R G Henry⁸, G Nair⁹, J Oh¹⁰, N Papinutto⁸, D Pelletier⁸, D S Reich¹¹, W D Rooney¹², T D Satterthwaite⁶, W Stern⁸, K Prabhakaran⁶, N L Sicotte¹³, R T Shinohara², J Goldsmith¹⁴; NAIMS Cooperative¹⁵

Affiliations

¹ Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, USA. Electronic address: julia.wrobel@cuanschutz.edu.
² Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
³ 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.
⁴ Department of Neurology, the Johns Hopkins University School of Medicine, Baltimore, MD, USA.
⁵ Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA.
⁶ Brain Behavior Laboratory, Department of Psychiatry, Neuropsychiatry Section, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
⁷ 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.
⁸ Department of Neurology, University of California - San Francisco, San Francisco, CA, USA.
⁹ Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA.
¹⁰ Department of Neurology, the Johns Hopkins University School of Medicine, Baltimore, MD, USA; St. Michael's Hospital, University of Toronto, Toronto, Ontario, Canada.
¹¹ 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.
¹² Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR, USA.
¹³ Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
¹⁴ Department of Biostatistics, Mailman School of Public Health, Columbia University, USA.
¹⁵ The North American Imaging in Multiple Sclerosis (NAIMS) Cooperative.

Abstract

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.

Keywords: Elsarticle.cls; Image harmonization; Intensity normalization; Multisite imaging; Warping.

Publication types

Research Support, N.I.H., Extramural
Research Support, N.I.H., Intramural
Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Artifacts
Brain / anatomy & histology*
Brain / diagnostic imaging*
Brain Mapping / methods*
Humans
Image Processing, Computer-Assisted / methods*
Magnetic Resonance Imaging*
Male
Middle Aged
Reproducibility of Results

Abstract

Publication types

MeSH terms

Grants and funding