Deep Learning Multi-channel Structural and Diffusion Tensor Neonatal Image Registration

Investigating the maturation process of the brain through clinical studies is crucial for advancing our understanding of the developing brain. Neuroimaging studies employing magnetic resonance images (MRI) often necessitate pre-alignment of images to a standardized space. Registration of diffusion tensor images (DTI) has the potential to better align white matter (WM) structures than using structural MRI only, as it enables alignment of fiber orientation at each voxel. However, microstructural data are not well suited to accurately register the cortical gray matter (cGM) ribbon. In spite of this, in many studies the alignment of subjects is primarily guided by a single modality. In this work, we propose a multi-channel attention-based deep learning registration approach that selects the most salient features from multiple image modalities to improve alignment of individual MR images to a common atlas space. We apply the technique to align multi-channel datasets composed of structural \(T_2\) -weighted ( \(T_2\) w) MRI and DTI maps into atlas space. The quantitative and qualitative evaluation confirmed that when we use the two modalities we obtain good alignment of anatomical structures, while also improving the alignment of the underlying white matter tracts. Moreover, we show that learning a spatially-varying attention map for weighting the two modalities obtains superior performance than the baseline multi-channel method that does not incorporate attention.