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Spatial normalization of multiple sclerosis brain MRI data depends on analysis method and software package.

Salina Pirzada1, Md Nasir Uddin2, Teresa D Figley2

  • 1Department of Physiology and Pathophysiology, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada; Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Health Sciences Centre, Winnipeg, MB, Canada.

Magnetic Resonance Imaging
|February 3, 2020
PubMed
Summary
This summary is machine-generated.

Comparing brain MRI spatial normalization methods for multiple sclerosis (MS) studies, SPM (CAT12) nonlinear warping showed superior performance, especially with lesion-filling, for accurate group comparisons.

Keywords:
ANTsBrainFSLLesionMRIMRIStudioMultiple sclerosisSPMSpatial normalizationWarping

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Area of Science:

  • Neuroimaging
  • Medical Image Analysis
  • Computational Neuroscience

Background:

  • Spatial normalization of brain MRI is crucial for comparing individuals and groups.
  • Multiple sclerosis (MS) pathologies can hinder automated spatial normalization accuracy.
  • Evaluating normalization methods is essential for reliable MS neuroimaging research.

Purpose of the Study:

  • To systematically compare five common brain MRI spatial normalization methods.
  • To assess the performance of linear and nonlinear algorithms in the presence of MS pathologies.
  • To identify the optimal spatial normalization technique for MS neuroimaging.

Main Methods:

  • Compared linear (affine) and nonlinear (MRIStudio, FSL, ANTs, SPM) normalization methods.
  • Used real MS patient data and simulated lesion data (T1-weighted and T2-FLAIR).
  • Quantified inter-subject variability using mutual information (MI) and coefficient of variation (COV).

Main Results:

  • All nonlinear methods outperformed linear normalization.
  • SPM (CAT12) demonstrated the highest MI, lowest COV, and best lesion volume scaling.
  • Lesion-filling provided minor improvements compared to algorithm differences.

Conclusions:

  • SPM (CAT12) nonlinear warping is recommended for MS brain imaging studies.
  • Combining SPM (CAT12) with lesion-filling is ideal for spatial normalization in MS.
  • This finding enhances the reliability of neuroimaging analysis in multiple sclerosis research.