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Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
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Is correction for gradient nonlinearity necessary in a brain diffusion tensor MRI clinical study?

Praitayini Kanakaraj1, Tianyuan Yao1, Zhiyuan Li1

  • 1Department of Computer Science, Vanderbilt University, Nashville, Tennessee, United States of America.

Plos One
|July 6, 2026
PubMed
Summary

Gradient nonlinearity correction in diffusion MRI is crucial for accurate brain imaging. Even subtle nonlinear field effects can impact microstructural and macrostructural measures, influencing clinical interpretations in aging and neurological studies.

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Area of Science:

  • Neuroimaging
  • Medical Physics
  • Radiology

Background:

  • Nonlinear gradients in diffusion tensor imaging (DTI) introduce spatially varying diffusion weighting, potentially biasing quantitative measures.
  • While often considered minimal, the cumulative impact of uncorrected gradient nonlinearity (GNL) on clinical outcomes warrants investigation.

Purpose of the Study:

  • To investigate the significance and effects of correcting GNL in diffusion-weighted MRI (DW-MRI).
  • To assess GNL's impact on microstructural and macrostructural changes in white and gray matter across a clinical cohort.
  • To determine if GNL alters the interpretation of aging and neurological conditions.

Main Methods:

  • Analysis of 948 DTI imaging sessions from the Vanderbilt Memory & Aging Project.
  • Assessment of GNL effects on individual scans, interscanner variability, and tract-based analysis.
  • Evaluation of head positioning variability (offsets and rotations) and its impact on GNL.

Main Results:

  • GNL correction revealed microstructural changes (FA, MD, V1) up to 5% in over 20% of the brain.
  • Macrostructural measures showed changes up to 12% post-correction.
  • GNL accounted for 5% of FA and 0.33% of MD changes between mild cognitive impairment and control groups.

Conclusions:

  • Subtle GNL effects can significantly impact quantitative DTI measures, particularly in subcortical and lobular regions.
  • GNL correction is essential for accurate interpretation of aging and neurological conditions in clinical research.
  • These effects become statistically detectable in larger multi-site studies, necessitating GNL consideration or correction.