Visualization, Interaction and Tractometry: Dealing with Millions of Streamlines from Diffusion MRI Tractography
Visualization, Interaction and Tractometry: Dealing with Millions of Streamlines from Diffusion MRI Tractography
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. Recently such an approach 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 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 memory 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.
diffusion MRI, tractometry, tractography, linearization, connectomics, compression, MI-Brain, Neuroscience, streamlines
diffusion MRI, tractometry, tractography, linearization, connectomics, compression, MI-Brain, Neuroscience, streamlines
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