Accurate and efficient co-registration of diffusion and T1-weighted MRI using self-supervised deep learning

Motivation: The co-registration between diffusion and T1-weighted data is important for various diffusion analyses, which is challenging due to the geometric distortion in diffusion images. Goal(s): To achieve accurate and efficient co-registration between diffusion data and T1w image. Approach: A self-supervised deep learning-based framework VoxelMorph was used to non-linearly align distorted diffusion b=0 image to T1w image. Our proposal was systematically and quantitatively compared to other linear and non-linear transformations. The benefit was also demonstrated. Results: VoxelMorph achieved comparable co-registration accuracy compared to NiftyReg and seconds processing time, which was 40 times faster than NiftyReg, or even 300 times faster by leveraging transfer learning. Impact: Our proposal achieved fast and accurate co-registration between distorted diffusion data and T1w image, which has a great potential to benefit various diffusion MRI data analyses for neuroscientific studies, including region-of-interest specific quantification and surface-based analysis.