Visualization, Interaction and Tractometry: Dealing with Millions of Streamlines from Diffusion MRI Tractography - Rheault, Francois; Houde, Jean-Christophe; Descoteaux, Maxime (Frontiers in Neuroinformatics, 2017)
Recently proposed tractography and connectomics approaches often require a very large number of streamlines, in the order of millions. Generating, storing and interacting with these datasets is currently quite difficult, since they require a lot of space in memory and processing time. Compression is a common approach to reduce data size, a recent method has been proposed consisting in removing collinear points in the streamlines. Removing points from streamlines results in files that cannot be robustly post-processed and interacted with with existing tools, which are for the most part point-based. The aim of this work is to improve visualization, interaction and tractometry algorithms to robustly handle compressed tractography datasets. Our proposed improvements are threefold: i) An efficient loading procedure to improve visualization (reduce memomry usage up to 95% for a 0.2 mm step size); ii) interaction techniques robust to compressed tractograms; iii) tractometry techniques robust to compressed tractograms to eliminate biased in tract-based statistics. The present work demonstrates the need of correctly handling compressed streamlines to avoid biases in future tractometry and connectomics studies.