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Updated: Sep 23, 2025
Diffusion Imaging in the Rat Cervical Spinal Cord
Published on: April 7, 2015
Reproducibility of the Standard Model of diffusion in white matter on clinical MRI systems
Santiago Coelho1, Steven H Baete1, Gregory Lemberskiy1
1Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, USA.
Abstract:
Estimating intra- and extra-axonal microstructure parameters, such as volume fractions and diffusivities, has been one of the major efforts in brain microstructure imaging with MRI. The Standard Model (SM) of diffusion in white matter has unified various modeling approaches based on impermeable narrow cylinders embedded in locally anisotropic extra-axonal space. However, estimating the SM parameters from a set of conventional diffusion MRI (dMRI) measurements is ill-conditioned. Multidimensional dMRI helps resolve the estimation degeneracies, but there remains a need for clinically feasible acquisitions that yield robust parameter maps. Here we find optimal multidimensional protocols by minimizing the mean-squared error of machine learning-based SM parameter estimates for two 3T scanners with corresponding gradient strengths of 40and80mT/m. We assess intra-scanner and inter-scanner repeatability for 15-minute optimal protocols by scanning 20 healthy volunteers twice on both scanners. The coefficients of variation all SM parameters except free water fraction are ≲10% voxelwise and 1-4% for their region-averaged values. As the achieved SM reproducibility outcomes are similar to those of conventional diffusion tensor imaging, our results enable robust in vivo mapping of white matter microstructure in neuroscience research and in the clinic.
Insights
This study optimized multidimensional diffusion MRI protocols for robust brain microstructure imaging. The new methods provide reliable Standard Model parameter mapping, crucial for neuroscience and clinical applications.
Area of Science:
- Neuroimaging
- Biophysics
- Medical Physics
Background:
- Estimating brain microstructure parameters like volume fractions and diffusivities is key in MRI.
- The Standard Model (SM) for white matter diffusion is limited by ill-conditioned parameter estimation from conventional dMRI.
- Multidimensional dMRI offers improvements, but clinically feasible, robust protocols are needed.
Purpose of the Study:
- To identify optimal multidimensional diffusion MRI protocols for robust Standard Model parameter estimation.
- To evaluate the feasibility of these protocols on clinical 3T scanners.
- To assess the repeatability and reproducibility of the parameter mapping.
Main Methods:
- Optimized multidimensional diffusion MRI protocols by minimizing machine learning-based SM parameter estimation error.
- Tested protocols on two 3T MRI scanners (40 and 80 mT/m gradient strengths).
- Assessed intra- and inter-scanner repeatability using 15-minute scans in 20 healthy volunteers.
Main Results:
- Achieved voxelwise coefficients of variation <10% for most SM parameters (excluding free water fraction).
- Region-averaged parameter values showed 1-4% coefficients of variation.
- Reproducibility outcomes were comparable to conventional diffusion tensor imaging.
Conclusions:
- Developed optimized, clinically feasible multidimensional dMRI protocols for robust white matter microstructure mapping.
- Demonstrated high repeatability and reproducibility of Standard Model parameter estimation.
- Results support the use of these advanced dMRI techniques in neuroscience research and clinical practice.
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