Calamante Fernando
Florey Institute of Neuroscience and Mental Health, Melbourne Brain Centre, 245 Burgundy Street, Heidelberg, VIC, 3084, Australia.
Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, VIC, Australia.
MAGMA. 2017 Aug;30(4):317-335. doi: 10.1007/s10334-017-0608-1. Epub 2017 Feb 8.
A whole-brain streamlines data-set (so-called tractogram) generated from diffusion MRI provides a wealth of information regarding structural connectivity in the brain. Besides visualisation strategies, a number of post-processing approaches have been proposed to extract more detailed information from the tractogram. One such approach is based on exploiting the information contained in the tractogram to generate track-weighted (TW) images. In the track-weighted imaging (TWI) approach, a very large number of streamlines are often generated throughout the brain, and an image is then computed based on properties of the streamlines themselves (e.g. based on the number of streamlines in each voxel, or their average length), or based on the values of an associated image (e.g. a diffusion anisotropy map, a T map) measured at the coordinates of the streamlines. This review article describes various approaches used to generate TW images and discusses the flexible formalism that TWI provides to generate a range of images with very different contrast, as well as the super-resolution properties of the resulting images. It also explains how this approach provides a powerful means to study structural and functional connectivity simultaneously. Finally, a number of key issues for its practical implementation are discussed.
由扩散磁共振成像生成的全脑流线数据集（即所谓的纤维束成像）提供了大量有关大脑结构连接性的信息。除了可视化策略外，还提出了许多后处理方法，以便从纤维束成像中提取更详细的信息。其中一种方法是利用纤维束成像中包含的信息来生成轨迹加权（TW）图像。在轨迹加权成像（TWI）方法中，通常会在整个大脑中生成大量流线，然后根据流线本身的属性（例如，基于每个体素中的流线数量或它们的平均长度），或者基于在流线坐标处测量的相关图像（例如，扩散各向异性图、T图）的值来计算图像。这篇综述文章描述了用于生成TW图像的各种方法，并讨论了TWI提供的灵活形式，以生成一系列具有非常不同对比度的图像，以及所得图像的超分辨率特性。它还解释了这种方法如何提供一种强大的手段来同时研究结构和功能连接性。最后，讨论了其实际应用中的一些关键问题。
