Removing inter-subject technical variability in magnetic resonance imaging studies

Abstract

Magnetic resonance imaging (MRI) intensities are acquired in arbitrary units, making scans non-comparable across sites and between subjects. Intensity normalization is a first step for the improvement of comparability of the images across 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 in large multi-site neuroimaging studies. We propose RAVEL (Removal of Artificial Voxel Effect by Linear regression), a tool to remove residual technical variability after intensity normalization. As proposed by SVA and RUV [Leek and Storey, 2007, 2008, Gagnon-Bartsch and Speed, 2012], two batch effect correction tools largely used in genomics, 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), where intensities are known to be unassociated with disease status and other clinical covariates. We perform a singular value decomposition (SVD) of the control voxels to estimate factors of unwanted variation. We then estimate the unwanted factors using linear regression for every voxel of the brain and take the residuals as the RAVEL-corrected intensities. 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 compare RAVEL to two intensity-normalization-only methods: histogram matching and White Stripe. We show that RAVEL performs best at improving the replicability of the brain regions that are empirically found to be most associated with AD, and that these regions are significantly more present in structures impacted by AD (hippocampus, amygdala, parahippocampal gyrus, enthorinal area and fornix stria terminals). In addition, we show that the RAVEL-corrected intensities have the best performance in distinguishing between MCI subjects and healthy subjects using the mean hippocampal intensity (AUC=67%), a marked improvement compared to results from intensity normalization alone (AUC=63% and 59% for histogram matching and White Stripe, respectively). RAVEL is promising for many other imaging modalities.

Harmonization of multi-site diffusion tensor imaging data

“…This is not surprising; both of these histogram-normalization methods fail to account for the spatial heterogeneity of the site effects throughout the brain. We also compared ComBat to RAVEL, an intensity normalization technique previously proposed for T1-w images [Fortin et al, 2016a]. RAVEL performed well for the FA maps, for which the FA values in the CSF reflect the technical variation in the WM.…”

“…We propose to use and adapt five statistical harmonization techniques for DTI data: global scaling, functional normalization [Fortin et al, 2014], RAVEL [Fortin et al, 2016a], Surrogate Variable Analysis (SVA)[Leek and Storey, 2007, 2008] and ComBat [Johnson et al, 2007]. We refer to the absence of harmonization as “raw” data.…”

“…The RAVEL algorithm described in Fortin et al [2016a] attempts to estimate a voxel-specific unwanted variation term by using a control region of the brain to estimate latent factors of unwanted variation common to all voxels. It is an extension of a previous intensity normalization, called White Stripe [Shinohara et al, 2014], developed to normalize white matter intensities in structural MRI.…”

“…It is an extension of a previous intensity normalization, called White Stripe [Shinohara et al, 2014], developed to normalize white matter intensities in structural MRI. Similar to the control region used in Fortin et al [2016a], we use voxels labelled as CSF as a control region. Theoretically, the FA values in CSF should be near 0 and similar across participants.…”

“…In this work, we adapted and compared several statistical approaches for the harmonization of DTI studies that were previously developed for other data types: Functional normalization [Fortin et al, 2014], RAVEL [Fortin et al, 2016a], Surrogate variable analysis (SVA) [Leek and Storey, 2007] and ComBat [Johnson et al, 2007], a popular batch adjustment method developed for genomics data. We also include a simple method that globally rescales the data for each site using a z-score transformation map common to all features, which we refer to as “global scaling”.…”