title: Training shortest-path tractography: Automatic learning of spatial priors.
abstract: Tractography is the standard tool for automatic delineation of white matter tracts from diffusion weighted images. However, the output of tractography often requires post-processing to remove false positives and ensure a robust delineation of the studied tract, and this demands expert prior knowledge. Here we demonstrate how such prior knowledge, or indeed any prior spatial information, can be automatically incorporated into a shortest-path tractography approach to produce more robust results. We describe how such a prior can be automatically generated (learned) from a population, and we demonstrate that our framework also retains support for conventional interactive constraints such as waypoint regions. We apply our approach to the open access, high quality Human Connectome Project data, as well as a dataset acquired on a typical clinical scanner. Our results show that the use of a learned prior substantially increases the overlap of tractography output with a reference atlas on both populations, and this is confirmed by visual inspection. Furthermore, we demonstrate how a prior learned on the high quality dataset significantly increases the overlap with the reference for the more typical yet lower quality data acquired on a clinical scanner. We hope that such automatic incorporation of prior knowledge and the obviation of expert interactive tract delineation on every subject, will improve the feasibility of large clinical tractography studies.
authors.affiliation: Department of Computer Science, University of Copenhagen, Denmark. Electronic address: niklas.kasenburg@di.ku.dk.
authors.forename: Niklas
authors.initials: N
authors.lastname: Kasenburg
authors.affiliation: Department of Computer Science, University of Copenhagen, Denmark; DTU Compute, Technical University of Denmark, Denmark.
authors.forename: Matthew
authors.initials: M
authors.lastname: Liptrot
authors.affiliation: DRCMR, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Denmark.
authors.forename: Nina Linde
authors.initials: NL
authors.lastname: Reislev
authors.affiliation: Department of Computer Science, University of Copenhagen, Denmark.
authors.forename: Silas N
authors.initials: SN
authors.lastname: Ørting
authors.affiliation: Department of Computer Science, University of Copenhagen, Denmark.
authors.forename: Mads
authors.initials: M
authors.lastname: Nielsen
authors.affiliation: DRCMR, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Denmark.
authors.forename: Ellen
authors.initials: E
authors.lastname: Garde
authors.affiliation: Department of Computer Science, University of Copenhagen, Denmark.
authors.forename: Aasa
authors.initials: A
authors.lastname: Feragen
mesh_terms: D000465:Algorithms; D056324:Diffusion Tensor Imaging / Q000379:methods*; D006801:Humans; D000069550:Machine Learning
keywords: Diffusion MRI; Graph theory; Prior information; Tractography