Keywords: Analysis/Processing, Machine Learning/Artificial Intelligence, co-registration, distortion correction, voxelmorph
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.
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