Deformable image registration between Computed Tomography (CT) images and
Magnetic Resonance (MR) imaging is essential for many image-guided therapies.
In this paper, we propose a novel translation-based unsupervised deformable
image registration method. Distinct from other translation-based methods that
attempt to convert the multimodal problem (e.g., CT-to-MR) into a unimodal
problem (e.g., MR-to-MR) via image-to-image translation, our method leverages
the deformation fields estimated from both: (i) the translated MR image and
(ii) the original CT image in a dual-stream fashion, and automatically learns
how to fuse them to achieve better registration performance. The multimodal
registration network can be effectively trained by computationally efficient
similarity metrics without any ground-truth deformation. Our method has been
evaluated on two clinical datasets and demonstrates promising results compared
to state-of-the-art traditional and learning-based methods.

提出一种新颖的基于翻译的无监督可变形图像配准方法，该方法利用双流方式调节变形场以实现更好的配准性能，并使用计算效率高的相似度度量对多模态配准网络进行有效训练，并在两个临床数据集上验证了其优越性能。