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Removing inter-subject technical variability in magnetic resonance imaging studies.

Jean-Philippe Fortin1, Elizabeth M Sweeney1, John Muschelli1

  • 1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.

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|March 1, 2016
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Summary

Magnetic resonance imaging (MRI) data variability is reduced by RAVEL (Removal of Artificial Voxel Effect by Linear regression). This new method improves the comparability of neuroimaging studies, enhancing Alzheimer's disease research.

Keywords:
ADNIAlzheimer's diseaseMRINormalizationScan effect

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Area of Science:

  • Neuroimaging
  • Medical Image Analysis
  • Biostatistics

Background:

  • Magnetic resonance imaging (MRI) intensities are in arbitrary units, hindering cross-site and cross-subject comparability.
  • Standard intensity normalization methods leave residual technical variability in large multi-site neuroimaging studies.
  • This variability can confound results in studies of neurodegenerative diseases like Alzheimer's disease (AD).

Purpose of the Study:

  • To introduce RAVEL (Removal of Artificial Voxel Effect by Linear regression), a novel tool to remove residual technical variability in MRI data after intensity normalization.
  • To evaluate RAVEL's performance in improving image comparability and enhancing the detection of AD-related brain changes.

Main Methods:

  • RAVEL decomposes voxel intensities into biological and unwanted variation components using linear regression.
  • Unwanted variation is estimated from cerebrospinal fluid (CSF) control regions and singular value decomposition (SVD).
  • RAVEL was tested on T1-weighted (T1-w) MRI data from over 900 subjects (AD, MCI, healthy controls) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.

Main Results:

  • RAVEL significantly improved the replicability of brain regions associated with AD, including the hippocampus and amygdala.
  • RAVEL-corrected data showed superior performance in distinguishing between mild cognitive impairment (MCI) and healthy subjects using mean hippocampal intensity (AUC=67%) compared to intensity normalization alone (AUC=63% and 59%).

Conclusions:

  • RAVEL effectively removes residual technical variability in MRI data, enhancing cross-site and cross-subject comparability.
  • The method shows promise for improving the sensitivity and specificity of neuroimaging biomarkers for Alzheimer's disease and other conditions.
  • RAVEL is a valuable tool for large-scale neuroimaging studies and may be applicable to other imaging modalities.