A kernel-based approach for user-guided fiber bundling using diffusion tensor data.
This paper describes a novel user-guided method for grouping fibers from diffusion tensor MRI tractography into bundles. The method finds fibers, that passing through user-defined ROIs, still fit to the underlying data model given by the diffusion tensor. This is achieved by filtering the data and the ROIs with a kernel derived from a geodesic metric between tensors. A standard approach using binary decisions defining tracts passing through ROIs is critically dependent on ROIs that includes all trace lines of interest. The method described in this paper uses a softer decision mechanism through a kernel which enables grouping of bundles driven less exact, or even single point, ROIs. The method analyzes the responses obtained from the convolution with a kernel function along the fiber with the ROI data. Results in real data shows the feasibility of the approach to fiber bundling.
San José Estépar
Raúl
R
Lab. of Math. in Imaging, Brigham & Women's Hosp., Boston, MA, USA. rjosest@bwh.harvard.edu
Kubicki
Marek
M
Shenton
Martha
M
Westin
Carl-Fredrik
CF
D000465:Algorithms; D001185:Artificial Intelligence; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D009412:Nerve Fibers / Q000648:ultrastructure*; D009434:Neural Pathways / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity