Groupwise registration of infant brain diffusion tensor images using intermediate subgroup templates
Kuaikuai Duan, Longchuan Li, Vince D. Calhoun, Sarah Shultz, Federico Giove, Federico Giove, Federico Giove
TL;DR
This paper introduces a new method for aligning infant brain images using tensor data and intermediate subgroup templates, improving registration accuracy.
Contribution
The novel approach uses subgroup tensor templates and clustering to enhance registration accuracy in infant brain imaging.
Findings
The proposed method significantly improves global and local registration accuracy compared to existing methods.
Clustering based on image similarity outperforms no clustering and matches age-based clustering in accuracy.
Abstract
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…
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Taxonomy
TopicsAdvanced Neuroimaging Techniques and Applications · Neonatal and fetal brain pathology · Fetal and Pediatric Neurological Disorders