Taquet Maxime, Scherrer Benoit, Commowick Olivier, Peters Jurriaan, Sahin Mustafa, Macq Benoît, Warfield Simon K
Computational Radiology Laboratory, Children's Hospital Boston, Harvard, USA.
Med Image Comput Comput Assist Interv. 2012;15(Pt 3):313-20. doi: 10.1007/978-3-642-33454-2_39.
Diffusion magnetic resonance imaging has been used extensively to probe the white matter in vivo. Typically, the raw diffusion images are used to reconstruct a diffusion tensor image (DTI). The incapacity of DTI to represent crossing fibers leaded to the development of more sophisticated diffusion models. Among them, multi-fiber models represent each fiber bundle independently, allowing the direct extraction of diffusion features for population analysis. However, no method exists to properly register multi-fiber models, seriously limiting their use in group comparisons. This paper presents a registration and atlas construction method for multi-fiber models. The validity of the registration is demonstrated on a dataset of 45 subjects, including both healthy and unhealthy subjects. Morphometry analysis and tract-based statistics are then carried out, proving that multi-fiber models registration is better at detecting white matter local differences than single tensor registration.
扩散磁共振成像已被广泛用于在体探测白质。通常,原始扩散图像用于重建扩散张量图像(DTI)。DTI无法表示交叉纤维,这促使了更复杂的扩散模型的发展。其中,多纤维模型独立表示每个纤维束,允许直接提取扩散特征以进行群体分析。然而,目前不存在适当注册多纤维模型的方法,这严重限制了它们在组间比较中的应用。本文提出了一种多纤维模型的配准和图谱构建方法。在一个包含45名受试者(包括健康和不健康受试者)的数据集上验证了配准的有效性。然后进行形态计量学分析和基于束的统计,证明多纤维模型配准在检测白质局部差异方面比单张量配准更好。