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von Miser-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。

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.