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Optimization and numerical evaluation of multi-compartment diffusion MRI using the spherical mean technique for

Sean P Devan1, Xiaoyu Jiang2, Francesca Bagnato3

  • 1Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN 37232, USA.

Magnetic Resonance Imaging
|September 8, 2020
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Summary

Spherical Mean Technique (SMT) diffusion MRI shows promise for multiple sclerosis (MS) imaging by characterizing lesions. While some biases exist, SMT offers valuable insights for clinical applications.

Keywords:
AxonDiffusionMRIMSMicrostructureSMT

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Area of Science:

  • Neuroimaging
  • Biomedical Engineering
  • Medical Physics

Background:

  • Multi-compartment diffusion MRI with Spherical Mean Technique (SMT) is proposed for enhanced pathological specificity in multiple sclerosis (MS) imaging.
  • Comprehensive evaluation of SMT accuracy and precision for MS tissue injury detection is lacking.

Purpose of the Study:

  • To optimize an SMT protocol for MS imaging using the Cramer-Rao Lower Bound method.
  • To assess the impact of realistic MS lesion pathological features on SMT metrics through computer simulations.

Main Methods:

  • Optimized SMT protocol using Cramer-Rao Lower Bound.
  • Simulated diffusion MRI data using finite difference method for spins in packed cylinders.
  • Evaluated effects of axon diameter, axon density, free water fraction, axonal crossing, dispersion, and undulation.

Main Results:

  • SMT-derived metrics can be influenced by pathological variations like axon size and free water fraction.
  • Despite potential biases, SMT provides valuable information for characterizing MS lesions.
  • A clinically feasible SMT protocol can yield informative metrics.

Conclusions:

  • SMT is a practical imaging method for MS.
  • Further improvements are needed for optimal clinical application in MS imaging.