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.

通过引入新的微分算子到注册框架中，以保证速度场的平滑性，结合交叉坐标注意力模块和多分辨率注册架构，改进了无监督可变形图像配准方法，在两个磁共振成像（MRI）数据集上进行了评估实验，表现出卓越的准确度、可变形性能和注册速度。