Deep Learning-Driven Enhancement of Fibre Orientation Distribution: Effect of Choice of Gradient Direction Number

Motivation: Learning-based methods effectively enhance fibre orientation distributions (FODs) derived from limited single-shell acquisitions. However, the enhancement capacity with different number of gradient directions is not fully characterised. Goal(s): This study aims to explore the impact of initial gradient directions on FOD enhancement capacity of clinically accessible single-shell diffusion data. Approach: We employ a FOD enhancement framework on single-shell (b=1000) data with different numbers of gradient directions. The enhanced FODs and derivatives are evaluated through FOD-based, fixel-based and connectome analysis metrics. Results: The optimal trade-off between the learning-based FOD enhancement outcome and the choice of number of gradient directions is at around 24 directions. Impact: This work provides guidelines for the optimal design of dMRI acquisition protocols meeting the expectations of clinical research on FOD enhancement according to the capability of learning-based frameworks, ensuring high-quality tractography and connectomes without the need for multi-shell HARDI protocols.