College of Electronics and Information Engineering, Sichuan University, PR China.
J Neurosci Methods. 2009 Nov 15;184(2):375-9. doi: 10.1016/j.jneumeth.2009.07.032. Epub 2009 Aug 8.
Diffusion tensor imaging (DTI) tractography is a novel technique that can delineate the trajectories between cortical region of the human brain non-invasively. In this paper, a novel DTI based white matter fiber tractography using genetic algorithm is presented. Adapting the concepts from evolutionary biology which include selection, recombination and mutation, globally optimized fiber pathways are generated iteratively. Global optimality of the fiber tracts is evaluated using Bayes decision rule, which simultaneously considers both the fiber geometric smoothness and consistency with the tensor field. This global optimality assigns the tracking fibers great immunity to random image noise and other local image artifacts, thus avoiding the detrimental effects of cumulative noise on fiber tracking. Experiments with synthetic and in vivo human DTI data have demonstrated the feasibility and robustness of this new fiber tracking technique, and an improved performance over commonly used probabilistic fiber tracking.
弥散张量成像(DTI)示踪技术是一种新颖的能够无创性描绘人类大脑皮质区域之间轨迹的技术。在本文中,提出了一种基于遗传算法的新型 DTI 白质纤维束追踪方法。该方法从进化生物学中引入选择、重组和突变等概念,通过迭代生成全局最优的纤维路径。通过贝叶斯决策规则来评估纤维束的全局最优性,该规则同时考虑了纤维的几何平滑度和与张量场的一致性。这种全局最优性使得跟踪纤维对随机图像噪声和其他局部图像伪影具有很强的免疫力,从而避免了累积噪声对纤维追踪的不利影响。使用合成和活体人类 DTI 数据的实验证明了这种新的纤维追踪技术的可行性和鲁棒性,并且比常用的概率纤维追踪技术具有更好的性能。