中文摘要：

为了解决概率纤维跟踪算法“过度”误跟踪，效率低的缺点，受蚁群路径搜索过程群体协作模式启发，提出一种全局脑白质纤维群体智能跟踪方法。首先，构建了一种全局纤维度量指标，综合考虑局部纤维方向分布和全局纤维走向，并利用贝叶斯方法建立局部纤维方向分布不确定信息模型。其次，提出一种群智能全局优化算法。该算法构建基于yonMiser-fisher分布函数的信息素模型，通过信息素模型诱导迭代优化纤维轨迹。人工合成数据实验结果表明，跟概率跟踪算法相比，该算法解决了纤维局部误差积累导致的误跟踪问题，相对误差降低至原来的二分之一，计算规模降低至原来的十分之一。实际临床数据验证了算法的有效性。

英文摘要：

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 Ima ging） data. Our method is inspired by the ant colony optimization technique, which considers both： the local fiber orien tation distribution and the global fiber path in a collaborative manner. We first construct a global optimization model that captures both global fiberpath and the uncertainties in local fiber orientation between two regions. The local fiberorienta tion density function with uncertainty is modeled with a Bayesian approach. Then, an ant colony global fibertracking algo rithm 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 yon MisesFisher 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.