Diffusion MRI experimental design optimization for microstructure imaging.

Area of Science:

• Neuroimaging
• Biophysics
• Computational Neuroscience

Background:

• Diffusion MRI (dMRI) is crucial for characterizing brain tissue microstructure.
• Current dMRI protocol optimization methods have limitations in unifying diverse biophysical models for white and gray matter.
• Existing approaches like Fisher information, subsampling, and machine learning lack a comprehensive optimization strategy.

Purpose of the Study:

• To propose a generalized framework for optimizing dMRI acquisition protocols.
• To enhance the accuracy of microstructural parameter estimation in brain tissue.
• To provide a unified approach for diverse biophysical models in both white and gray matter.

Main Methods:

• Developed a generalized protocol design framework based on the Cramér-Rao Lower Bound (CRLB).
• Implemented biophysical models in a differentiable environment for gradient computation using automatic differentiation.
• Optimized protocols across parameter spaces while accommodating user-defined constraints.

Main Results:

• The proposed CRLB-based framework enables stable and reproducible convergence for high-dimensional, nonlinear models.
• Automatic differentiation avoided approximation errors and scalability limitations of traditional optimization methods.
• Optimized protocols demonstrably improved the estimation accuracy of microstructural metrics (e.g., axonal diameter indices, diffusivities, water-exchange rates).

Conclusions:

• The generalized CRLB framework offers a robust and scalable method for dMRI protocol optimization.
• This approach enhances the fidelity of biophysical modeling for brain microstructure analysis.
• The findings have significant implications for advancing research and clinical applications of diffusion MRI.