Comparison of Novel ICA-Based Approach to Existing Diffusion MRI Multi-Fiber Reconstruction Methods

Bryce Wilkins¹, Namgyun Lee¹, Kyungmin Nam¹, Darryl Hwang¹, Manbir Singh¹

A novel Independent Component Analysis (ICA) based approach to resolving multiple fiber directions per voxel in diffusion-weighted MRI analysis is quantitatively compared against four alternatives: Generalized q-Sampling Imaging, Constrained Spherical Deconvolution, analytical Q-Ball Imaging, and Higher-Order Tensors. We investigate the performance of the various methods when processing limited sample data, as is likely to be acquired in clinical studies due to constrained scan time. Results of two phantom datasets and a human study are presented, revealing consistently higher metric scores for the ICA-based approach, especially in the case of limited gradient sampling.