Diffusion MRI experimental design optimization for microstructure imaging

Accurate characterization of brain tissue microstructure using diffusion MRI (dMRI) depends on data acquisition protocols. While optimal design has been explored through Fisher information, empirical subsampling of large datasets and machine learning strategies, a unified approach of optimization for diverse white and gray matter biophysical models remains necessary. To this end, we propose a generalized protocol design framework based on the Cramér-Rao Lower Bound (CRLB), which accommodates user-defined constraints while comprehensively optimizing across parameter spaces. By implementing biophysical models in a differentiable environment to compute gradients using automatic differentiation, we avoid the approximation errors of finite-difference methods and the scalability limitations of derivative-free optimization. This enables stable, reproducible convergence for high-dimensional, nonlinear models. Moreover, this approach produces data acquisition protocols that improve the estimation accuracy of microstructural metrics such as axonal diameter indices, diffusivities, and compartment water-exchange rates, thereby enhancing the fidelity of biophysical modeling for research and clinical applications. Experimental design optimization for diffusion MRI improves brain tissue microstructure estimation. The framework uses the Cramér-Rao Lower Bound and automatic differentiation to optimize data acquisition for diverse white and gray matter models.