Duan Kuaikuai, Li Longchuan, Calhoun Vince D, Shultz Sarah
Marcus Autism Center, Children's Healthcare of Atlanta, Atlanta, Georgia USA.
Emory University School of Medicine, Department of Pediatrics, Atlanta, Georgia, USA.
bioRxiv. 2024 Jul 16:2024.07.12.603305. doi: 10.1101/2024.07.12.603305.
Registering longitudinal infant brain images is challenging, as the infant brain undergoes rapid changes in size, shape and tissue contrast in the first months and years of life. Diffusion tensor images (DTI) have relatively consistent tissue properties over the course of infancy compared to commonly used T1 or T2-weighted images, presenting great potential for infant brain registration. Moreover, groupwise registration has been widely used in infant neuroimaging studies to reduce bias introduced by predefined atlases that may not be well representative of samples under study. To date, however, no methods have been developed for groupwise registration of tensor-based images. Here, we propose a novel registration approach to groupwise align longitudinal infant DTI images to a sample-specific common space. Longitudinal infant DTI images are first clustered into more homogenous subgroups based on image similarity using Louvain clustering. DTI scans are then aligned within each subgroup using standard tensor-based registration. The resulting images from all subgroups are then further aligned onto a sample-specific common space. Results show that our approach significantly improved registration accuracy both globally and locally compared to standard tensor-based registration and standard fractional anisotropy-based registration. Additionally, clustering based on image similarity yielded significantly higher registration accuracy compared to no clustering, but comparable registration accuracy compared to clustering based on chronological age. By registering images groupwise to reduce registration bias and capitalizing on the consistency of features in tensor maps across early infancy, our groupwise registration framework facilitates more accurate alignment of longitudinal infant brain images.
对婴儿脑部纵向图像进行配准具有挑战性，因为婴儿大脑在生命的最初几个月和几年中，其大小、形状和组织对比度会发生快速变化。与常用的T1或T2加权图像相比，扩散张量图像（DTI）在婴儿期的过程中具有相对一致的组织特性，这为婴儿脑部配准提供了巨大潜力。此外，群体配准已广泛应用于婴儿神经影像学研究，以减少由预定义图谱引入的偏差，这些图谱可能不能很好地代表所研究的样本。然而，迄今为止，尚未开发出用于基于张量的图像进行群体配准的方法。在此，我们提出一种新颖的配准方法，将婴儿DTI纵向图像群体对齐到特定样本的公共空间。首先，使用Louvain聚类基于图像相似性将婴儿DTI纵向图像聚类为更同质的子组。然后，使用基于标准张量的配准在每个子组内对齐DTI扫描。然后，将所有子组得到的图像进一步对齐到特定样本的公共空间。结果表明，与基于标准张量的配准和基于标准分数各向异性的配准相比，我们的方法在全局和局部上均显著提高了配准精度。此外，与不进行聚类相比，基于图像相似性的聚类产生了显著更高的配准精度，但与基于年龄顺序的聚类相比，配准精度相当。通过群体配准图像以减少配准偏差，并利用婴儿早期张量图中特征的一致性，我们的群体配准框架有助于更准确地对齐婴儿脑部纵向图像。
