Kochunov P, Lancaster J, Thompson P, Boyer A, Hardies J, Fox P
Research Imaging Center, University of Texas Health Science Center at San Antonio, USA.
Hum Brain Mapp. 2000 Nov;11(3):193-206. doi: 10.1002/1097-0193(200011)11:3<193::AID-HBM50>3.0.CO;2-Z.
The goal of regional spatial normalization is to remove anatomical differences between individual three-dimensional (3D) brain images by warping them to match features of a standard brain atlas. Processing to fit features at the limiting resolution of a 3D MR image volume is computationally intensive, limiting the broad use of full-resolution regional spatial normalization. In Kochunov et al. (1999: Neuro-Image 10:724-737), we proposed a regional spatial normalization algorithm called octree spatial normalization (OSN) that reduces processing time to minutes while targeting the accuracy of previous methods. In the current study, modifications of the OSN algorithm for use in human brain images are described and tested. An automated brain tissue segmentation procedure was adopted to create anatomical templates to drive feature matching in white matter, gray matter, and cerebral-spinal fluid. Three similarity measurement functions (fast-cross correlation (CC), sum-square error, and centroid) were evaluated in a group of six subjects. A combination of fast-CC and centroid was found to provide the best feature matching and speed. Multiple iterations and multiple applications of the OSN algorithm were evaluated to improve fit quality. Two applications of the OSN algorithm with two iterations per application were found to significantly reduce volumetric mismatch (up to six times for lateral ventricle) while keeping processing time under 30 min. The refined version of OSN was tested with anatomical landmarks from several major sulci in a group of nine subjects. Anatomical variability was appreciably reduced for every sulcus investigated, and mean sulcal tracings accurately followed sulcal tracings in the target brain.
区域空间归一化的目标是通过对个体三维（3D）脑图像进行变形，使其与标准脑图谱的特征相匹配，从而消除个体脑图像之间的解剖差异。在3D MR图像体素的极限分辨率下进行拟合特征的处理计算量很大，限制了全分辨率区域空间归一化的广泛应用。在科丘诺夫等人（1999年：《神经图像》10：724 - 737）的研究中，我们提出了一种称为八叉树空间归一化（OSN）的区域空间归一化算法，该算法将处理时间缩短至几分钟，同时达到了先前方法的精度。在当前研究中，描述并测试了用于人类脑图像的OSN算法的改进版本。采用自动脑组织分割程序来创建解剖模板，以驱动白质、灰质和脑脊液中的特征匹配。在一组六名受试者中评估了三种相似性测量函数（快速互相关（CC）、均方误差和质心）。发现快速CC和质心的组合提供了最佳的特征匹配和速度。评估了OSN算法的多次迭代和多次应用，以提高拟合质量。发现OSN算法进行两次应用，每次应用进行两次迭代，可显著减少体积不匹配（侧脑室最多减少六倍），同时将处理时间控制在30分钟以内。在一组九名受试者中，使用来自几个主要脑沟的解剖标志对OSN的改进版本进行了测试。对于所研究的每个脑沟，解剖变异性都明显降低，平均脑沟追踪准确地跟随目标脑中的脑沟追踪。
