Geng Xiujuan, Christensen Gary E, Gu Hong, Ross Thomas J, Yang Yihong
Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, USA.
Neuroimage. 2009 Oct 1;47(4):1341-51. doi: 10.1016/j.neuroimage.2009.04.024. Epub 2009 Apr 14.
In this study, an implicit reference group-wise (IRG) registration with a small deformation, linear elastic model was used to jointly estimate correspondences between a set of MRI images. The performance of pair-wise and group-wise registration algorithms was evaluated for spatial normalization of structural and functional MRI data. Traditional spatial normalization is accomplished by group-to-reference (G2R) registration in which a group of images are registered pair-wise to a reference image. G2R registration is limited due to bias associated with selecting a reference image. In contrast, implicit reference group-wise (IRG) registration estimates correspondences between a group of images by jointly registering the images to an implicit reference corresponding to the group average. The implicit reference is estimated during IRG registration eliminating the bias associated with selecting a specific reference image. Registration performance was evaluated using segmented T1-weighted magnetic resonance images from the Nonrigid Image Registration Evaluation Project (NIREP), DTI and fMRI images. Implicit reference pair-wise (IRP) registration-a special case of IRG registration for two images-is shown to produce better relative overlap than IRG for pair-wise registration using the same small deformation, linear elastic registration model. However, IRP-G2R registration is shown to have significant transitivity error, i.e., significant inconsistencies between correspondences defined by different pair-wise transformations. In contrast, IRG registration produces consistent correspondence between images in a group at the cost of slightly reduced pair-wise RO accuracy compared to IRP-G2R. IRG spatial normalization of the fractional anisotropy (FA) maps of DTI is shown to have smaller FA variance compared with G2R methods using the same elastic registration model. Analyses of fMRI data sets with sensorimotor and visual tasks show that IRG registration, on average, increases the statistical detectability of brain activation compared to G2R registration.
在本研究中,使用具有小变形的线性弹性模型的隐式参考组对组(IRG)配准来联合估计一组MRI图像之间的对应关系。评估了成对和组对组配准算法在结构和功能MRI数据空间归一化方面的性能。传统的空间归一化是通过组到参考(G2R)配准来完成的,其中一组图像成对地配准到一个参考图像。由于与选择参考图像相关的偏差,G2R配准存在局限性。相比之下,隐式参考组对组(IRG)配准通过将图像联合配准到与组平均值对应的隐式参考来估计一组图像之间的对应关系。在IRG配准过程中估计隐式参考,消除了与选择特定参考图像相关的偏差。使用来自非刚性图像配准评估项目(NIREP)的分割T1加权磁共振图像、DTI和fMRI图像评估配准性能。隐式参考成对(IRP)配准——IRG配准用于两张图像的特殊情况——在使用相同的小变形线性弹性配准模型进行成对配准时,显示出比IRG产生更好的相对重叠。然而,IRP - G2R配准显示出显著的传递性误差,即由不同成对变换定义的对应关系之间存在显著不一致。相比之下,IRG配准以与IRP - G2R相比成对RO精度略有降低为代价,在一组图像之间产生一致的对应关系。与使用相同弹性配准模型的G2R方法相比,DTI分数各向异性(FA)图的IRG空间归一化显示出较小的FA方差。对具有感觉运动和视觉任务的fMRI数据集的分析表明,与G2R配准相比,IRG配准平均提高了大脑激活的统计可检测性。