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Test-retest reliability of Neurite Orientation Dispersion and Density Imaging (NODDI) diffusion MRI models is validated. Faster acquisitions and specific fitting algorithms enhance reliability for clinical translation of brain microstructure biomarkers.

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Area of Science:

  • Neuroimaging
  • Diffusion MRI
  • Brain Microstructure Analysis

Background:

  • Neurite Orientation Dispersion and Density Imaging (NODDI) and Bingham-NODDI are key diffusion MRI models for brain microstructure estimation.
  • Assessing the reproducibility and reliability of NODDI biomarkers is crucial for clinical translation.
  • Test-retest studies are essential for validating the precision of different fitting toolboxes.

Purpose of the Study:

  • To evaluate the impact of various factors on the reliability of diffusion models (NODDI, Bingham-NODDI).
  • Factors investigated include fitting algorithms, multiband acceleration, shell configuration, subject age, and hemispheric side.
  • Reliability was assessed using Intra-class Correlation Coefficient (ICC) and Variation Factor (VF).

Main Methods:

  • A test-retest study design was employed using data from pediatric and adult subjects.
  • Simultaneous-MultiSlice (SMS) imaging with two acceleration factors (AF) and four b-values were used.
  • Data were fitted using two GPU-based algorithms across seven shell configurations.

Main Results:

  • Higher acceleration factors (AF) demonstrated very good ICC values, indicating reliability is maintained with faster acquisitions and reduced motion artifacts.
  • Shell configurations showed minor reliability differences, but lower b-value shells increased variability for simple models like NODDI.
  • Fitting tools significantly impacted reliability, independent of acquisition parameters, suggesting inherent differences in algorithm performance.

Conclusions:

  • A 10-minute multi-shell diffusion MRI acquisition protocol yields reliable results for NODDI in white matter (WM).
  • More complex models like Bingham-NODDI show greater sensitivity to true subject variability, not necessarily less reliable with reduced data.
  • These findings support the clinical utility of NODDI and advanced diffusion models for precise brain microstructure assessment.