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Diffusion Imaging in the Rat Cervical Spinal Cord
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Estimating axial diffusivity in the NODDI model.

Amy Fd Howard1, Michiel Cottaar1, Mark Drakesmith2

  • 1FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.

Neuroimage
|August 5, 2022
PubMed
Summary
This summary is machine-generated.

This study reveals that the assumed intra-axonal axial diffusivity in diffusion MRI models significantly impacts parameter estimates. We show how to estimate this diffusivity directly from high b-value data, improving accuracy for neurite imaging.

Keywords:
Axial diffusivityDiffusion MRIHigh b-valueNODDIOrientation dispersionWhite matter

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Area of Science:

  • Neuroimaging
  • Biophysical modeling
  • Diffusion MRI

Background:

  • Biophysical models in diffusion MRI rely on simplifying assumptions about tissue microstructure.
  • The validity of these assumptions, particularly for intra-axonal axial diffusivity, is not well-established.
  • Discrepancies exist between literature values and assumed values in models like Neurite Orientation Dispersion and Density Imaging (NODDI).

Purpose of the Study:

  • To investigate the impact of assumed axial diffusivity on NODDI parameter estimates.
  • To develop and validate a method for estimating axial diffusivity as a free parameter using high b-value data.
  • To assess the influence of noise characteristics and fitting strategies in low signal-to-noise ratio (SNR) regimes.

Main Methods:

  • Adapted the NODDI framework to estimate axial diffusivity as a free parameter.
  • Utilized simulated and in vivo diffusion MRI data with high b-values.
  • Investigated the effects of fitting to real-valued vs. magnitude data and Gaussian vs. Rician noise models.
  • Analyzed the impact of noise floor rectification and signal offset correction.

Main Results:

  • Varying the assumed axial diffusivity led to significantly different NODDI parameter estimates.
  • Estimated intra-axonal axial diffusivities from human data were approximately 2-2.5 µm²/ms, aligning with literature.
  • Incorrect noise assumptions in low SNR conditions can bias parameter estimates.
  • Accounting for a rectified noise floor and/or signal offset is crucial for accurate low SNR data analysis.

Conclusions:

  • The assumed axial diffusivity is a critical parameter influencing diffusion MRI model outputs.
  • Estimating axial diffusivity directly from high b-value data improves model robustness.
  • Accurate modeling of noise characteristics, including noise floor and signal offset, is essential for reliable diffusion MRI parameter estimation, especially at high b-values.