title: Probabilistic shortest path tractography in DTI using Gaussian Process ODE solvers.
abstract: Tractography in diffusion tensor imaging estimates connectivity in the brain through observations of local diffusivity. These observations are noisy and of low resolution and, as a consequence, connections cannot be found with high precision. We use probabilistic numerics to estimate connectivity between regions of interest and contribute a Gaussian Process tractography algorithm which allows for both quantification and visualization of its posterior uncertainty. We use the uncertainty both in visualization of individual tracts as well as in heat maps of tract locations. Finally, we provide a quantitative evaluation of different metrics and algorithms showing that the adjoint metric (8] combined with our algorithm produces paths which agree most often with experts.
authors.forename: Michael
authors.initials: M
authors.lastname: Schober
authors.forename: Niklas
authors.initials: N
authors.lastname: Kasenburg
authors.forename: Aasa
authors.initials: A
authors.lastname: Feragen
authors.forename: Philipp
authors.initials: P
authors.lastname: Hennig
authors.forename: Soren
authors.initials: S
authors.lastname: Hauberg
mesh_terms: D000465:Algorithms; D001921:Brain / Q000166:cytology*; D063132:Connectome / Q000379:methods*; D003627:Data Interpretation, Statistical; D056324:Diffusion Tensor Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D009413:Nerve Fibers, Myelinated / Q000648:ultrastructure*; D016011:Normal Distribution; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity