Abstract #2165

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

Xinyi Wang^1,2, Zihao Tang^1,2, Mariano Cabezas², Arkiev D’Souza², Dongnan Liu^1,2, Michael Barnett^2,3, Fernando Calamante^2,4,5, Chenyu Wang^2,3, and Weidong Cai¹

¹School of Computer Science, The University of Sydney, Sydney, Australia, ²Brain and Mind Centre, The University of Sydney, Sydney, Australia, ³Sydney Neuroimaging Analysis Centre, The University of Sydney, Sydney, Australia, ⁴School of Biomedical Engineering, The University of Sydney, Sydney, Australia, ⁵Sydney Imaging, The University of Sydney, Sydney, Australia

Synopsis

Keywords: Diffusion Analysis & Visualization, Tractography & Fibre Modelling, Fiber Orientation Distribution, Brain Connectivity, Enhancement

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.

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