Abstract

Tractography is a visualization technique which reconstructs and mod-els neural fibers in the white matter of the brain based on data from dif-fusion magnetic resonance imaging. It is already used locally to model parts of dominant fiber pathways but global methods are also emerging which aim to reconstruct all the brain fibers simultaneously. In this thesis we have attempted to improve the current state of the art of Global Tractography by introducing three principles: • Anatomical Priors • Introduction of fiber weights • Reduced complexity Our approach uses an optimization method based on Markov Chain Monte Carlo (MCMC) and Simulated annealing in order to fit a set of plausi-ble initial fiber trajectories to a dataset acquired by diffusion MRI. Our