Duan Kuaikuai, Li Longchuan, Calhoun Vince D, Shultz Sarah
Marcus Autism Center, Children's Healthcare of Atlanta, Atlanta, Georgia, United States of America.
Emory University School of Medicine, Department of Pediatrics, Atlanta, Georgia, United States of America.
PLoS One. 2025 Jun 26;20(6):e0325844. doi: 10.1371/journal.pone.0325844. eCollection 2025.
Registering infant brain images is challenging, as the infant brain undergoes rapid changes in size, shape and tissue contrast in the first months 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 using intermediate templates can reduce deformation and bias introduced by predefined atlases, but most methods use scalar (e.g., fractional anisotropy) images, which lack the microstructural orientation information in tensor images that can help differentiate brain structures and further improve infant image registration accuracy. Here, we propose an intermediate subgroup tensor template-based groupwise (IST-G tensor) registration approach to align infant tensor images to a sample-specific common space. First, tensor images are clustered into more homogenous subgroups using Louvain clustering based on image similarity. Within each subgroup, tensor images are aligned using DTI-toolkit to generate subgroup tensor templates, which are subsequently aligned to a sample-specific common space. Results show that our approach significantly improved registration accuracy both globally and locally compared to standard tensor-based and fractional anisotropy-based approaches. Clustering based on image similarity yielded significantly higher registration accuracy than no clustering and performed comparably to clustering by chronological age. By leveraging the consistency of features in tensor maps across early infancy and reducing deformation through intermediate subgroup tensor templates, our IST-G tensor registration framework facilitates more accurate alignment of longitudinal infant brain tensor images.
对婴儿脑部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里，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，使用中间模板进行分组配准可以减少预定义图谱引入的变形和偏差，但大多数方法使用标量（例如，分数各向异性）图像，这些图像缺乏张量图像中的微观结构方向信息，而这些信息有助于区分脑结构并进一步提高婴儿图像配准的准确性。在此，我们提出一种基于中间子组张量模板的分组（IST-G张量）配准方法，将婴儿张量图像对齐到特定样本的公共空间。首先，基于图像相似性，使用Louvain聚类将张量图像聚类为更均匀的子组。在每个子组内，使用DTI工具包对齐张量图像以生成子组张量模板，随后将这些模板对齐到特定样本的公共空间。结果表明，与基于标准张量和基于分数各向异性的方法相比，我们的方法在全局和局部上都显著提高了配准精度。基于图像相似性的聚类产生的配准精度明显高于无聚类，并且与按年龄顺序聚类相当。通过利用婴儿早期张量图中特征的一致性，并通过中间子组张量模板减少变形，我们的IST-G张量配准框架有助于更准确地对齐纵向婴儿脑张量图像。
