对称归一化配准的多图谱大脑扩散张量图像分割

Segmentation of brain diffusion tensor images on multi-atlas for symmetric normalized registration

基于磁共振T1成像的全脑区分割方法及其在临床上的应用已经有较为广泛的研究,磁共振扩散张量成像凭借其在脑白质神经纤维束成像上的优势,近年来围绕扩散张量成像的理论和应用研究发展很快,针对扩散张量成像的脑区分割研究就成为一个必须要解决的问题。图像配准是精确实现脑区分割的重要技术步骤,传统的配准方法未考虑到图像的形变大小对配准精度的影响,最新的对称配准策略是解决这一问题的最好选择。因此,考虑到扩散张量图像的分辨率低的特点,以及对称配准的技术先进性,提出了一种自动方法进行了扩散张量图像的全脑分区,并利用统计学SPSS软件对阿尔茨海默症(AD)患者与正常对照组(NC)数据进行了比较研究,验证了所提出的脑区分割方法的准确性。

The whole brain segmentation method based on magnetic resonance T1 imaging and its clinical application have been extensively studied.With its advantages in imaging of white matter nerve fiber tracts,the theory and application of diffusion sensor imaging(DTI)has developed rapidly in recent years,and the study of brain segmentation for diffusion tensor imaging has become a problem that must be solved.Image registration is an important technical step to accurately realize brain segmentation.Traditional registration methods do not take into account the influence of image distortion on registration accuracy.The latest symmetric registration strategy is the best choice to solve this problem.Therefore,considering the characteristics of low resolution of diffusion tensor images and the technological advancement of symmetric registration,an automatic method to partition the whole brain of diffusion tensor images was proposed,and statistical SPSS software was used to compare AD patients and NC control group,and the accuracy of the proposed brain segmentation method was verified.