RABBIT: Rapid alignment of brains by building intermediate templates
RABBIT: Rapid alignment of brains by building intermediate templates
复制标题
DOI：
10.1016/j.neuroimage.2009.02.043
复制
发表时间：
2009-10-01
期刊：
影响因子：
5.7
通讯作者：
Shen, Dinggang
中科院分区：
文献类型：
--
作者：
Tang, Songyuan;Fan, Yong;Shen, Dinggang
为了实现快速准确的脑图像配准，提出了一种基于统计形变模型生成的中间模板的脑图像配准算法。统计变形模型是通过对大脑变形场的一组训练样本进行主成分分析(PCA)来建立的，所述训练样本将选定的模板图像扭曲到各个大脑样本。统计形变模型能够通过少量的参数来表征个体的脑变形，这些参数用于快速估计模板和新的个体脑图像之间的脑变形。然后使用估计的变形来扭曲模板，从而生成接近单个大脑图像的中间模板。最后，通过图像配准算法，例如HAMMER算法，估计中间模板和个体大脑之间的形状差异。通过将使模板扭曲的变形场直接组合到中间模板，并将中间模板组合到单独的脑图像，可以实现模板和单个脑图像之间的整体配准。该算法已被用于模拟和真实磁共振成像(MRI)脑图像的空间归一化。实验结果表明，与HAMR算法相比，该算法在检测脑萎缩时可以获得5倍以上的加速比，而配准精度和统计能力相当。(C)2009 Elsevier Inc.保留所有权利。
A brain image registration algorithm, referred to as RABBIT, is proposed to achieve fast and accurate image registration with the help of an intermediate template generated by a statistical deformation model. The statistical deformation model is built by principal component analysis (PCA) on a set of training samples of brain deformation fields that warp a selected template image to the individual brain samples. The statistical deformation model is capable of characterizing individual brain deformations by a small number of parameters, which is used to rapidly estimate the brain deformation between the template and a new individual brain image. The estimated deformation is then used to warp the template, thus generating an intermediate template close to the individual brain image. Finally, the shape difference between the intermediate template and the individual brain is estimated by an image registration algorithm, e.g., HAMMER. The overall registration between the template and the individual brain image can be achieved by directly combining the deformation fields that warp the template to the intermediate template, and the intermediate template to the individual brain image. The algorithm has been validated for spatial normalization of both Simulated and real magnetic resonance imaging (MRI) brain images. Compared with HAMMER, the experimental results demonstrate that the proposed algorithm can achieve over five times speedup, with similar registration accuracy and statistical power in detecting brain atrophy. (C) 2009 Elsevier Inc. All rights reserved.