Revolutionizing Diffusion MRI Alignment: Integrating Deep Learning with Advanced Registration Techniques

Diffusion Magnetic Resonance Imaging (dMRI) has emerged as a groundbreaking imaging technique, capturing the intricate motion of water molecules within biological tissues, particularly the neural fibers in the brain. The precision in aligning dMRI data is pivotal for subsequent analytical processes, such as tractography or group studies. This paper introduces three innovative algorithms that amalgamate the prowess of deep learning with state-of-the-art registration techniques, aiming to redefine the landscape of dMRI alignment. The first algorithm, DeepRegNet, harnesses deep learning to directly ascertain the required transformation for alignment. The second, Diffusion Tensorbased Registration (DTR), utilizes the inherent diffusion tensor information in dMRI data, aligning images based on tensor matching. The third, DeepDiffusionNet, is a fusion of deep learning and non-linear warping, ensuring accurate alignment with smooth deformations. Through rigorous testing, these algorithms have demonstrated their potential in enhancing the accuracy and efficiency of dMRI alignment.