EPDiff-JF-Net: Adjoint Jacobi Fields for Diffeomorphic Registration Networks

Large Deformation Diffeomorphic Metric Mapping (LDDMM) is a well-established diffeomorphic registration method, a critical step for many medical imaging applications. Of special interest are its geodesic shooting variants, which enable statistical shape analysis from transformations usable in computational anatomy studies. This paper introduces a novel deep learning-based unsupervised approach for diffeomorphic image registration called EPDiff-JF-Net. Our method predicts an initial velocity field, performs geodesic shooting to obtain the corresponding path of diffeomorphisms, and utilizes an adjoint Jacobi fields layer to calculate the relevant gradients for training from parallel transport along the geodesic. The model is trained in a fully unsupervised end-to-end manner, with no requirement of ground-truth in the training loss. Experimental results on 3D brain MRI datasets demonstrate the effectiveness of EPDiff-JF-Net, outperforming EPDiff-based LDDMM and deep learning methods while significantly reducing computation time.