Summary: In this paper, we propose a fast and novel probabilistic fiber tracking method for Diffusion tensor imaging (DTI) data using the ant colony tracking technique, which considers both the local fiber orientation distribution and the global fiber path in collaborative manner. We first construct a global optimization model that captures both global fiber path and the uncertainties in local fiber orientation. Then, a global fiber tracking algorithm is presented using a novel learning strategy where the probability associated with a fiber is iteratively maximized. Finally, the proposed algorithm is validated and compared to alternative methods using both synthetic and real data.