Tang Songyuan, Fan Yong, Wu Guorong, Kim Minjeong, Shen Dinggang
Department of Radiology, University of North Carolina, Chapel Hill, NC 27510, USA.
Neuroimage. 2009 Oct 1;47(4):1277-87. doi: 10.1016/j.neuroimage.2009.02.043. Epub 2009 Mar 10.
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.
提出了一种称为RABBIT的脑图像配准算法，借助统计变形模型生成的中间模板实现快速准确的图像配准。统计变形模型是通过主成分分析（PCA）在一组脑变形场训练样本上构建的，这些样本将选定的模板图像扭曲到各个脑样本上。统计变形模型能够通过少量参数表征个体脑变形，这些参数用于快速估计模板与新的个体脑图像之间的脑变形。然后，利用估计的变形对模板进行扭曲，从而生成接近个体脑图像的中间模板。最后，通过图像配准算法（例如HAMMER）估计中间模板与个体脑之间的形状差异。通过直接组合将模板扭曲到中间模板以及将中间模板扭曲到个体脑图像的变形场，可以实现模板与个体脑图像之间的整体配准。该算法已针对模拟和真实磁共振成像（MRI）脑图像的空间归一化进行了验证。与HAMMER相比，实验结果表明，所提出的算法可以实现超过五倍的加速，在检测脑萎缩方面具有相似的配准精度和统计功效。
