Network Connectivity via Inference over Curvature-Regularizing Line Graphs

Abstract

Abstract. Diffusion Tensor Imaging (DTI) provides estimates of local directional information regarding paths of white matter tracts in the human brain. An important problem in DTI is to infer tract connectivity (and networks) from given image data. We propose a method that infers high-level network structures and connectivity information from Diffusion Tensor images. Our algorithm extends principles from perceptual contours to construct a weighted line-graph based on how well the tensors agree with a set of proposal curves (regularized by length and curvature). The problem of extracting high-level anatomical connectivity is then posed as an optimization problem over this curvature-regularizing graphwhich gives subgraphs which comprise a representation of the tracts' network topology. We present experimental results and an open-source implementation of the algorithm.

Cortico-Cortical, Cortico-Striatal, and Cortico-Thalamic White Matter Fiber Tracts Generated in the Macaque Brain via Dynamic Programming

“…Thus, a Markovian approach (Iglesias et al, 2012) could be used to incorporate bending energy into the cost function (Poupon et al, 2000) for regularization. Adjusting the state space and cost function to be based on voxel triples (Collins et al, 2011) allows for more sophisticated regularization. It is also possible to incorporate Bayesian priors based on tensor coherence or dot-product with regard to a neighboring voxel Here, the color code for the connection metric is not used.…”