Automated image registration: II. Intersubject validation of linear and nonlinear models
Automated image registration: II. Intersubject validation of linear and nonlinear models
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DOI：
10.1097/00004728-199801000-00028
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发表时间：
1998-01-01
影响因子：
1.3
通讯作者：
Mazziotta, JC
中科院分区：
文献类型：
--
作者：
Woods, RP;Grafton, ST;Mazziotta, JC
目的：我们的目标是验证线性和非线性intersubject图像配准使用自动化方法（AIR 3.0）的基础上voxel intensity.Method：PET和MRI数据从22个正常受试者注册到相应的平均PET或MRI脑图谱使用几个特定的线性和非线性空间变换模型与自动化算法。验证是基于解剖学定义的landmars.Results：自动注册产生的结果是优于手动9参数变体的Talairach注册方法的上级。在空间变换模型中增加自由度提高了自动intersubject registration.Conclusion的准确性：线性或非线性自动intersubject注册体素强度的基础上计算是实用的，并产生更准确的同源地标比手动9参数Talairach注册对齐。非线性模型比线性模型提供更好的配准，但速度较慢。
Purpose: Our goal was to validate linear and nonlinear intersubject image registration using an automated method (AIR 3.0) based on voxel intensity.Method: PET and MRI data from 22 normal subjects were registered to corresponding averaged PET or MRI brain atlases using several specific linear and nonlinear spatial transformation models with an automated algorithm. Validation was based on anatomically defined landmarks.Results: Automated registration produced results that were superior to a manual nine parameter variant of the Talairach registration method. Increasing the degrees of freedom in the spatial transformation model improved the accuracy of automated intersubject registration.Conclusion: Linear or nonlinear automated intersubject registration based on voxel intensities is computationally practical and produces more accurate alignment of homologous landmarks than manual nine parameter Talairach registration. Nonlinear models provide better registration than linear models but are slower.