Transformer-based 2D/3D medical image registration for X-ray to CT via anatomical features

2D/3D medical image registration is one of the key technologies for surgical navigation systems to perform pose estimation and achieve accurate positioning, which still remains challenging. The purpose of this study is to introduce a new method for X-ray to CT 2D/3D registration and conduct a feasibility study.

In this study, a 2D/3D affine registration method based on feature point detection is investigated. It combines the morphological and edge features of spinal images to accurately extract feature points from the images, and uses graph neural networks to aggregate anatomical features of different points to increase the local detail information. Meanwhile, global and positional information are extracted by the Swin Transformer.

The results indicate that the proposed method has shown improvements in both accuracy and success ratio compared with other methods. The mean target registration error value reached up to 0.31 mm; meanwhile, the runtime overhead was much lower, achieving an average runtime of about 0.6 s. This ultimately improves the registration accuracy and efficiency, demonstrating the effectiveness of the proposed method.

The proposed method can provide more comprehensive image information and shows good prospects for pose estimation and achieving accurate positioning in surgical navigation systems.