Removing Inter-Subject Technical Variability in Magnetic Resonance Imaging Studies

• Activity Number: 547
• Type: Contributed
• Date/Time: Wednesday, August 3, 2016 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics in Imaging
• Author(s): Jean-Philippe Fortin* and Elizabeth M. Sweeney and John Muschelli and Ciprian Crainiceanu and Russell Shinohara
• Companies: The Johns Hopkins University and Johns Hopkins Bloomberg School of Public Health and The Johns Hopkins University and The Johns Hopkins University and University of Pennsylvania
• Keywords: Intensity normalization ; Multi-center studies ; Structural MRI ; Unwanted variation ; Alzheimer's Disease ; ADNI

Abstract

Magnetic resonance imaging (MRI) intensities are acquired in arbitrary units, making scans non-comparable across sites and between subjects. However, we show that unwanted inter-scan variability associated with imaging site, scanner effect and other technical artifacts is still present after standard intensity normalization. We propose RAVEL (Removal of Artificial Voxel Effect by Linear regression), a tool to remove residual technical variability after intensity normalization. We decompose the voxel intensities of images registered to a template into a biological component and an unwanted variation component. The unwanted variation component is estimated from a control region obtained from the cerebrospinal fluid (CSF). We assess the performance of RAVEL using T1-weighted (T1-w) images from more than 900 subjects with Alzheimer's disease (AD) and mild cognitive impairment (MCI), as well as healthy controls from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We show that RAVEL performs best at improving the replicability of the brain regions that are empirically found to found to be most associated with AD.