Kochunov P V, Lancaster J L, Fox P T
Research Imaging Center, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio 78284, Texas, USA.
Neuroimage. 1999 Dec;10(6):724-37. doi: 10.1006/nimg.1999.0509.
The goal of regional spatial normalization is to remove anatomical differences between individual three-dimensional (3-D) brain images by warping them to match features of a standard brain atlas. Full-resolution volumetric spatial normalization methods use a high-degree-of-freedom coordinate transform, called a deformation field, for this task. Processing to fit features at the limiting resolution of a 3-D MR image volume is computationally intensive, limiting broad use of full-resolution regional spatial normalization. A highly efficient method, designed using an octree decomposition and analysis scheme, is presented to resolve the speed problem while targeting accuracy comparable to current volumetric methods. Translation and scaling capabilities of octree spatial normalization (OSN) were tested using computer models of solid objects (cubes and spheres). Boundary mismatch between transformed and target objects was zero for cubes and less than 1% for spheres. Regional independence of warping was tested using brain models consisting of a homogenous brain volume with one internal homogenous region (lateral ventricle). Boundary mismatch improved with successively smaller octant-level processing and approached levels of less than 1% for the brain and 5% for the lateral ventricle. Five 3-D MR brain images were transformed to a target 3-D brain image to assess boundary matching. Residual boundary mismatch was approximately 4% for the brain and 8% for the lateral ventricle, not as good as with homogeneous brain models, but similar to other results. Total processing time for OSN with a 256(3) brain image (1-mm voxel spacing) was less than 10 min.
区域空间归一化的目标是通过对个体三维（3-D）脑图像进行变形，使其与标准脑图谱的特征相匹配，从而消除这些图像之间的解剖学差异。全分辨率体积空间归一化方法使用一种高自由度坐标变换（称为变形场）来完成此任务。在3-D MR图像体积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。本文提出了一种使用八叉树分解和分析方案设计的高效方法，以解决速度问题，同时目标精度与当前体积法相当。使用固体物体（立方体和球体）的计算机模型测试了八叉树空间归一化（OSN）的平移和缩放能力。对于立方体，变换后的物体与目标物体之间的边界失配为零，对于球体则小于1%。使用由具有一个内部均匀区域（侧脑室）的均匀脑体积组成的脑模型测试了变形的区域独立性。随着八分体级处理逐渐变小，边界失配得到改善，对于脑接近小于1%的水平，对于侧脑室接近5%的水平。将五幅3-D MR脑图像变换为目标3-D脑图像以评估边界匹配。对于脑，残余边界失配约为4%，对于侧脑室为8%，不如均匀脑模型好，但与其他结果相似。使用256(3)脑图像（体素间距为1毫米）进行OSN的总处理时间不到10分钟。
