Sugiura M, Kawashima R, Sadato N, Senda M, Kanno I, Oda K, Sato K, Yonekura Y, Fukuda H
Department of Nuclear Medicine and Radiology, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan.
J Nucl Med. 1999 Feb;40(2):317-22.
Spatial normalization methods, which are indispensable for intersubject analysis in current PET studies, have been improved in many aspects. These methods have not necessarily been evaluated as anatomic normalization methods because PET images are functional images. However, in view of the close relation between brain function and morphology, it is very intriguing how precisely normalized brains coincide with each other. In this report, the anatomic precision of spatial normalization is validated with three different methods.
Four PET centers in Japan participated in this study. In each center, six normal subjects were recruited for both H2(15)O-PET and high-resolution MRI studies. Variations in the location of the anterior commissure (AC) and size and contours of the brain and the courses of major sulci were measured in spatially normalized MR images for each method. Spatial normalization was performed as follows. (a) Linear: The AC-posterior commissure and midsagittal plane were identified on MRI and the size of the brain was adjusted to the Talairach space in each axis using linear parameters. (b) Human brain atlas (HBA): Atlas structures were manually adjusted to MRI to determine linear and nonlinear transformation parameters and then MRI was transformed with the inverse of these parameters. (c) Statistical parametric mapping (SPM) 95: PET images were transformed into the template PET image with linear and nonlinear parameters in a least-squares manner. Then, coregistered MR images were transformed with the same parameters used for the PET transformation.
The AC was well registered in all methods. The size of the brain normalized with SPM95 varied to a greater extent than with other approaches. Larger variance in contours was observed with the linear method. Only SPM95 showed significant superiority to the linear method when the courses of major sulci were compared.
The results of this study indicate that SPM95 is as effective a spatial normalization as HBA, although it does not use anatomic images. Large variance in structures other than the AC and size of the brain in the linear method suggests the necessity of nonlinear transformations for effective spatial normalization. Operator dependency of HBA also must be considered.
空间归一化方法在当前PET研究的受试者间分析中不可或缺，且已在诸多方面得到改进。由于PET图像是功能图像，这些方法不一定被视为解剖归一化方法。然而，鉴于脑功能与形态之间的密切关系，精确归一化的大脑彼此间的契合程度究竟如何，这非常引人关注。在本报告中，用三种不同方法验证了空间归一化的解剖精度。
日本的四个PET中心参与了本研究。在每个中心，招募了六名正常受试者进行H2(15)O-PET和高分辨率MRI研究。对于每种方法，在空间归一化的MR图像中测量前连合（AC）位置、脑的大小和轮廓以及主要脑沟走行的变化。空间归一化按以下方式进行。(a) 线性：在MRI上识别AC-后连合和正中矢状面，并使用线性参数在每个轴上将脑的大小调整到Talairach空间。(b) 人脑图谱（HBA）：将图谱结构手动调整到MRI以确定线性和非线性变换参数，然后用这些参数的逆变换MRI。(c) 统计参数映射（SPM）95：PET图像以最小二乘法用线性和非线性参数变换为模板PET图像。然后，用用于PET变换的相同参数变换配准后的MR图像。
所有方法中AC的配准都很好。用SPM95归一化的脑大小变化程度比其他方法更大。线性方法观察到轮廓的方差更大。在比较主要脑沟走行时，只有SPM95显示出明显优于线性方法。
本研究结果表明，尽管SPM95不使用解剖图像，但它作为一种空间归一化方法与HBA一样有效。线性方法中除AC和脑大小外的结构存在较大方差，这表明有效空间归一化需要非线性变换。还必须考虑HBA的操作者依赖性。
