Diffusion Tensor Magnetic Resonance Image Registration Based on Parallel Dual-Channel VoxelMorph

Diffusion Tensor Magnetic Resonance Imaging (DTI) is a non-invasive technique for studying brain structure in vivo by measuring the diffusion properties of water molecules. Unlike conventional medical imaging that captures scalar intensity data, DTI data is typically stored as a 4D volume, where each voxel in 3D space is a 3×3 Cartesian tensor. DTI characterizes tensor-based diffusion profiles and captures information about the orientation of fiber bundles. During the alignment process, voxels need to be spatially transformed while maintaining the correspondence of tensor orientations, which leads to complex computations. Traditional DTI registration methods often suffer from slow iteration speed and low accuracy, posing challenges for clinical applications. In this paper, a novel DTI Registration method Based on Parallel Dual-channel Voxel Morph (DTI-RBPDV) is proposed. The core of the method is a two-branch convolutional neural network architecture. With a view to enhancing the alignment performance, it processes two input patterns simultaneously: (1) fractional anisotropy (FA) images and (2) principal eigenvectors from to-be-aligned and fixed DTI volumes to enhance the accuracy of deformation field prediction. In the network decoder layer, integration of attention mechanisms has also been implemented. These channel space attention modules dynamically highlight salient anatomical features and orientation consistency, improving the model's sensitivity to key structural alignments. Experimental results show that DTI-RBPDV effectively addresses the limitations of slow iterative computation and the challenges of applying deep learning to high-dimensional DTI data by significantly improving the registration accuracy and computational speed.