AUTOMATIC 3D INTERSUBJECT REGISTRATION OF MR VOLUMETRIC DATA IN STANDARDIZED TALAIRACH SPACE
AUTOMATIC 3D INTERSUBJECT REGISTRATION OF MR VOLUMETRIC DATA IN STANDARDIZED TALAIRACH SPACE
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DOI：
10.1097/00004728-199403000-00005
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发表时间：
1994-03-01
影响因子：
1.3
通讯作者：
EVANS, AC
中科院分区：
文献类型：
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作者：
COLLINS, DL;NEELIN, P;EVANS, AC
目的：在诊断和研究应用中，当可以通过视觉检查等效解剖平面比较不同的数据集时，促进了人脑MR图像的解释。使用预定义的地图集模板进行定量分析通常需要地图集和图像平面的初始对齐。不幸的是，在单独的扫描过程中获得的轴向平面通常在其相对位置和方向上通常是不同的，并且这些切片与地图集中的轴向平面并不是共面。我们已经开发了一种完全自动的方法，可以用Talairach立体定位坐标系注册给定的体积数据集。材料和方法：注册方法基于多尺度，三维（3D）互相关，平均（n> 300）MR大脑图像体积与Talairach立体定位空间对齐。一旦通过算法恢复的转换重新采样数据集后，Atlas切片就可以直接叠加在重新采样体积的相应切片上。这种标准化空间的使用还允许直接比较与体素的体素，两个或多个数据集带入立体定位空间。回报：使用对配对样品的两尾学生t测试，没有显着差异，在与两种手动里程碑的方法相比，自动算法恢复的转换参数（对于除y级以外的所有参数的P> 0.1，其中p> 0.05）。使用归一化体素强度之间的根平方差异作为一个公正的注册量度，我们表明，当估计和平均60次体积MR图像在标准空间中的平均值时，自动技术的量度比手动方法低30％，表明这表明更好的注册。同样，自动方法显示标准偏差降低了57％，这意味着一种更稳定的技术。即使在体积的顶部或底部缺少数据时，该算法也能够恢复转换。结论：我们提出了一种全自动的注册方法，将体积数据映射到立体定位空间中，从而产生的结果与手动基于手动的技术相当。该方法不需要手动识别点或轮廓，因此不会遭受用户干预涉及的缺点，例如可重复性和观察者间的可变性。
Objective: In both diagnostic and research applications, the interpretation of MR images of the human brain is facilitated when different data sets can be compared by visual inspection of equivalent anatomical planes. Quantitative analysis with predefined atlas templates often requires the initial alignment of atlas and image planes. Unfortunately, the axial planes acquired during separate scanning sessions are often different in their relative position and orientation, and these slices are not coplanar with those in the atlas. We have developed a completely automatic method to register a given volumetric data set with Talairach stereotaxic coordinate system.Materials and Methods: The registration method is based on multiscale, three-dimensional (3D) cross-correlation with an average (n > 300) MR brain image volume aligned with the Talairach stereotaxic space. Once the data set is resampled by the transformation recovered by the algorithm, atlas slices can be directly superimposed on the corresponding slices of the resampled volume. The use of such a standardized space also allows the direct comparison, voxel to voxel, of two or more data sets brought into stereotaxic space.Results: With use of a two-tailed Student t test for paired samples, there was no significant difference in the transformation parameters recovered by the automatic algorithm when compared with two manual landmark-based methods (p > 0.1 for all parameters except y-scale, where p > 0.05). Using root-mean-square difference between normalized voxel intensities as an unbiased measure of registration, we show that when estimated and averaged over 60 volumetric MR images in standard space, this measure was 30% lower for the automatic technique than the manual method, indicating better registrations. Likewise, the automatic method showed a 57% reduction in standard deviation, implying a more stable technique. The algorithm is able to recover the transformation even when data are missing from the top or bottom of the volume.Conclusion: We present a fully automatic registration method to map volumetric data into stereotaxic space that yields results comparable with those of manually based techniques. The method requires no manual identification of points or contours and therefore does not suffer the drawbacks involved in user intervention such as reproducibility and interobserver variability.