DiffOp-net: A Differential Operator-based Fully Convolutional Network
  for Unsupervised Deformable Image Registration
Jiong Wu
Existing unsupervised deformable image registration methods usually rely on
metrics applied to the gradients of predicted displacement or velocity fields
as a regularization term to ensure transformation smoothness, which potentially
limits registration accuracy. In this study, we propose a novel approach to
enhance unsupervised deformable image registration by introducing a new
differential operator into the registration framework. This operator, acting on
the velocity field and mapping it to a dual space, ensures the smoothness of
the velocity field during optimization, facilitating accurate deformable
registration. In addition, to tackle the challenge of capturing large
deformations inside image pairs, we introduce a Cross-Coordinate Attention
module (CCA) and embed it into a proposed Fully Convolutional Networks
(FCNs)-based multi-resolution registration architecture. Evaluation experiments
are conducted on two magnetic resonance imaging (MRI) datasets. Compared to
various state-of-the-art registration approaches, including a traditional
algorithm and three representative unsupervised learning-based methods, our
method achieves superior accuracies, maintaining desirable diffeomorphic
properties, and exhibiting promising registration speed.