Cheng Peng, Magnotta Vincent A, Wu Dee, Nopoulos Peg, Moser David J, Paulsen Jane, Jorge Ricardo, Andreasen Nancy C
Department of Radiology, Georgetown University, Washington, DC 20007, USA.
Neuroimage. 2006 Jul 1;31(3):1075-85. doi: 10.1016/j.neuroimage.2006.01.028. Epub 2006 May 2.
Fiber tracking, based on diffusion tensor imaging (DTI), is the only approach available to non-invasively study the three-dimensional structure of white matter tracts. Two major obstacles to this technique are partial volume artifacts and tracking errors caused by image noise. In this paper, a novel fiber tracking algorithm called Guided Tensor Restore Anatomical Connectivity Tractography (GTRACT) is presented. This algorithm utilizes a multi-pass approach to fiber tracking. In the first pass, a 3D graph search algorithm is utilized. The second pass incorporates anatomical connectivity information generated in the first pass to guide the tracking in this stage. This approach improves the ability to reconstruct complex fiber paths as well as the tracking accuracy. Validation and reliability studies using this algorithm were performed on both synthetic phantom data and clinical human brain data. A method is also proposed for the evaluating reliability of fiber tract generation based both on the position of the fiber tracts, as well the anisotropy values along the path. The results demonstrate that the GTRACT algorithm is less sensitive to image noise and more capable of handling areas of complex fiber crossing, compared to conventional streamline methods.
基于扩散张量成像（DTI）的纤维追踪是唯一可用于非侵入性研究白质束三维结构的方法。该技术的两个主要障碍是部分容积伪影和由图像噪声引起的追踪误差。本文提出了一种名为引导张量恢复解剖连接性纤维束成像（GTRACT）的新型纤维追踪算法。该算法采用多步方法进行纤维追踪。第一步，利用三维图搜索算法。第二步纳入第一步生成的解剖连接性信息，以指导该阶段的追踪。这种方法提高了重建复杂纤维路径的能力以及追踪准确性。使用该算法对合成体模数据和临床人脑数据进行了验证和可靠性研究。还提出了一种基于纤维束位置以及沿路径的各向异性值来评估纤维束生成可靠性的方法。结果表明，与传统的流线型方法相比，GTRACT算法对图像噪声不太敏感，并且更有能力处理复杂纤维交叉区域。
