基于大脑模板的MRI扫描自动定位方法

在大脑磁共振成像(MRI)影像学的数据采集中,通常先扫描一幅定位图像,并根据解剖学先验知识手动调整合适的扫描定位参数,再进行后续的正式扫描.该文实现了一种直接以大脑模板为参照的自动定位的方法:首先采集一幅中等分辨率的快速三维定位图像,然后通过与模板的配准确定定位参数,并应用到后续序列的扫描,以保证不同被试在图像采集时采用与模板一致的空间定位.该方法一方面便于不同被试的图像数据之间进行系统性比较与参照,帮助诊断者快速定位病灶,也可在后续常用的基于体素分析过程最大化数据的利用效率.另一方面,针对单个体多次扫描之间的自动定位,该文进一步使用迭代方法,通过多次"扫描、配准、自动定位"步骤,逐步减小图像配准算法的误差.实验证明,该文基于大脑模板的自动定位方法能够确保不同被试之间和同一被试之内在图像数据采集时的空间定位高度一致性,其中同一被试内多次扫描的空间定位误差1.0 mm和1.0o.

A Brain Atlas Based Automatic Localization Method for MRI

Acquisition of brain magnetic resonance imaging (MRI) data usually starts with a localizer for properly positioning the field of view based on a prior knowledge of brain anatomy and setting corresponding localization parameters for subsequent scans. We propose an automatic localization method that references directly to the brain atlas. The procedure first quickly acquires a 3D localization image at a median spatial resolution, and then calculates its registration parameter to the atlas and uses these parameters to position the subsequent scans, which therefore ensures the scanning configurations for different subjects are consistent with the atlas. The proposed method benefits inter-subject comparisons and referencing, in that it can help investigators locating abnormal structure, tumors or other regions-of-interest more quickly and easily, and therefore using the data in voxel based analysis more efficiently. We also propose an iterative method for automatic localizing individual subject in multiple independent follow-up scans. By iterating “scan, registration, automatic localization” steps several passes, it progressively minimizes the error of the image registration algorithm. Experiments showed that our atlas-based automatic localization method achieved high consistency of spatial location both in imaging data acquired from different subjects and in multiple separate scans from a single subject, and the localization error between multiple scans of a single subject was less than 1.0 mm and 1.0 degree.