Tench C R, Morgan P S, Blumhardt L D, Constantinescu C
Division of Clinical Neurology, University Hospital, Derby Road, Nottingham, UK.
Magn Reson Med. 2002 Oct;48(4):677-83. doi: 10.1002/mrm.10266.
Diffusion tensor imaging (DTI) promises a robust means of visualizing in vivo white matter fibers in individual subjects, and of inferring direct connectivity between distant points in the brain. By following the primary eigenvector of the diffusion tensor, trajectories may be defined that trace the path of the underlying fiber tract. However, fiber tracking is prone to cumulative error from acquisition noise and partial volume, which limits the repeatability of such techniques. An image-processing method based on stochastic labeling, by which the noisy primary eigenvectors may be reconfigured according to anatomically reasonable assumptions, is described. The method's potential to improve fiber tracking is first demonstrated on numerical test data. It is then applied to real data acquired from healthy volunteers. Trajectories defined within the corpus callosum and the pyramidal tracts are rendered using 3D graphic imaging software, and the results are compared before and after processing. Fiber tracking was shown to produce anatomically plausible results, and typical errors were largely resolved by the method. Further, the sensitivity of trajectories to their start point was greatly reduced after processing. The use of stochastic labeling may therefore improve the reliability of experiments using white matter fiber tracking.
扩散张量成像（DTI）有望成为一种强大的手段，用于在个体受试者体内可视化白质纤维，并推断大脑中远距离点之间的直接连接性。通过追踪扩散张量的主特征向量，可以定义出描绘潜在纤维束路径的轨迹。然而，纤维追踪容易受到采集噪声和部分容积的累积误差影响，这限制了此类技术的可重复性。本文描述了一种基于随机标记的图像处理方法，通过该方法可以根据解剖学上合理的假设重新配置有噪声的主特征向量。该方法改善纤维追踪的潜力首先在数值测试数据上得到了证明。然后将其应用于从健康志愿者获取的真实数据。使用3D图形成像软件绘制胼胝体和锥体束内定义的轨迹，并比较处理前后的结果。结果表明，纤维追踪产生了符合解剖学的合理结果，并且该方法在很大程度上解决了典型误差。此外，处理后轨迹对其起点的敏感性大大降低。因此，随机标记的使用可能会提高使用白质纤维追踪的实验的可靠性。
