Automated image registration: I. General methods and intrasubject, intramodality validation
Automated image registration: I. General methods and intrasubject, intramodality validation
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DOI：
10.1097/00004728-199801000-00027
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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：我们试图描述和验证自动图像配准方法（AIR 3.0）的基础上匹配的体素intensity.Method：不同的成本函数，不同的最小化方法，和各种采样，平滑，编辑策略进行了比较。内部一致性的措施被用来放置限制MRI数据的配准精度，和绝对精度进行测量，使用脑体模的PET data.Results：所有的策略是一致的subvoxel精度为intrasubject，intramodality注册。结构MRI图像配准的估计精度在75至150 μ m范围内。稀疏的数据采样策略减少注册时间分钟只有适度的损失accuracy.Conclusion：所描述的注册算法是一个强大的和灵活的工具，可用于解决各种图像配准问题。配准策略可以通过优化速度和准确性之间的权衡来定制以满足不同的需求。
Purpose: We sought to describe and validate an automated image registration method (AIR 3.0) based on matching of voxel intensities.Method: Different cost functions, different minimization methods, and various sampling, smoothing, and editing strategies were compared. Internal consistency measures were used to place limits on registration accuracy for MRI data, and absolute accuracy was measured using a brain phantom for PET data.Results: All strategies were consistent with subvoxel accuracy for intrasubject, intramodality registration. Estimated accuracy of registration of structural MRI images was in the 75 to 150 mu m range. Sparse data sampling strategies reduced registration times to minutes with only modest loss of accuracy.Conclusion: The registration algorithm described is a robust and flexible tool that can be used to address a variety of image registration problems. Registration strategies can be tailored to meet different needs by optimizing tradeoffs between speed and accuracy.