In order to deal with the limitation of probabilistic tractography which may produce diffuse results that suggest connections in unexpected regions

we propose a fast and novel global fiber tracking method for DTI (Diffusion Tensor Imaging)data. Our method is inspired by the ant colony optimization technique

which considers both:the local fiber orientation distribution and the global fiber path in a collaborative manner. We first construct a global optimization model that captures both global fiber-path and the uncertainties in local fiber orientation between two regions. The local fiber-orientation density function with uncertainty is modeled with a Bayesian approach. Then

an ant colony global fiber-tracking algorithm is presented using a new learning strategy where the probability associated with a fiber is iteratively maximized. In the algorithm

the pheromone model is constructed using the von Mises-Fisher function and the ant fiber tracking technique based on pheromone model is developed. Finally

the proposed algorithm is validated and compared to alternative methods using both synthetic and real data.