A novel streamline representation to reduce redundancy in tractography

Diffusion MRI tractography allows one to characterize brain connectivity in vivo, and it is common practice to reconstruct millions of streamlines and filter them a posteriori. However, redundancy among streamlines leads to collinearity in the linear operators used by existing filtering algorithms. To solve this problem, we propose a novel streamline representation which uses a combination of clustering and spatial blur to reduce redundancy. This representation is as accurate as state-of-the-art filtering methods and more robust to noise/perturbations in the input, but requires only ≈5% of the input streamlines thus decreasing both storage requirements and computational complexity.