Keywords: Tractography & Fibre Modelling, Tractography & Fibre ModellingDiffusion 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.
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