Removing inter-subject technical variability in magnetic resonance imaging studies

• Published in: NeuroImage (Orlando, Fla.) Vol. 132; pp. 198 - 212
• Main Authors: Fortin, Jean-Philippe, Sweeney, Elizabeth M., Muschelli, John, Crainiceanu, Ciprian M., Shinohara, Russell T.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15.05.2016; Elsevier Limited
• Subjects: ADNI; Algorithms; Alzheimer Disease - diagnostic imaging; Alzheimer Disease - pathology; Alzheimer's disease; Biomarkers; Brain - anatomy & histology; Brain - pathology; Datasets; Female; Gene expression; Humans; Image Processing, Computer-Assisted; Linear Models; Magnetic Resonance Imaging - methods; Male; Medical imaging; Methods; MRI; Neuroimaging - methods; NMR; Normalization; Nuclear magnetic resonance; Reproducibility of Results; ROC Curve; Scan effect; Scanners; Signal Processing, Computer-Assisted; Studies; ADNI; Normalization; MRI; Alzheimer's disease; Scan effect
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2016.02.036

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. •Between-scan unwanted variation is still present after intensity normalization.•We propose a scan-effect removal tool for removing post-normalization artifacts.•We model the unwanted variability between MRI T1-w scans using CSF region.•We use a large subset of the ADNI database to showcase our method.•We show that our method improves replicability of the voxels associated with AD.•MRI intensities corrected by our method improves prediction of AD and MCI.