QuickBundles, a Method for Tractography Simplification

By Eleftherios Garyfallidis, Matthew Brett, Marta Morgado Correia, Guy B. Williams, Ian Nimmo-Smith. Frontiers in Neuroscience, 2012

306 citations recorded by the source corpus.

Diffusion MR data sets produce large numbers of streamlines which are hard to visualize, interact with, and interpret in a clinically acceptable time scale, despite numerous proposed approaches. As a solution we present a simple, compact, tailor-made clustering algorithm, QuickBundles (QB), that overcomes the complexity of these large data sets and provides informative clusters in seconds. Each QB cluster can be represented by a single centroid streamline; collectively these centroid streamlines can be taken as an effective representation of the tractography. We provide a number of tests to show how the QB reduction has good consistency and robustness. We show how the QB reduction can help in the search for similarities across several subjects.

Subfields: Pediatrics, Perinatology and Child Health, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/966817d6b881abac5b48789248c7d09f411185ab

Citations in the corpus

• The challenge of mapping the human connectome based on diffusion tractography (2017, unknown): “Hence, we adopted an alternative de fi nition with less stringent criteria based on robust shape distance measures 76 and clustering between streamlines 77 , as detailed in Supplementary Note 4.”