  Image-based rigid 2D/3D registration is a critical technique for fluoroscopic
guided surgical interventions. In recent years, some learning-based fully
differentiable methods have produced beneficial outcomes while the process of
feature extraction and gradient flow transmission still lack controllability
and interpretability. To alleviate these problems, in this work, we propose a
novel fully differentiable correlation-driven network using a dual-branch
CNN-transformer encoder which enables the network to extract and separate
low-frequency global features from high-frequency local features. A
correlation-driven loss is further proposed for low-frequency feature and
high-frequency feature decomposition based on embedded information. Besides, a
training strategy that learns to approximate a convex-shape similarity function
is applied in our work. We test our approach on a in-house datasetand show that
it outperforms both existing fully differentiable learning-based registration
approaches and the conventional optimization-based baseline.
