BACKGROUND: Thoracic aortic endovascular repair is an important method for treating aortic dissection and thoracic aortic aneurysm. The success of the operation depends on whether the stent graft is placed in the correct position. However, when the stent is implanted, the aorta in the intraoperative X-ray image is invisible, so the operation is difficult and the risk is high. Registration of preoperative CT angiography and intraoperative X-ray images can help doctors place stents and increase success rates. OBJECTIVE: To propose a preoperative CT angiography and intraoperative X-ray image registration algorithm for thoracic aortic endovascular repair. METHODS: Firstly, digital reconstruction images of CT angiography and bone CT were performed under different virtual perspectives, and the two were superimposed to obtain a digital reconstruction image library under various angles of position and orientation for intraoperative X-ray images. Secondly, we proposed a deep neural network based on branch decoding structure. Using digital reconstruction image library training, the position and attitude parameters of intraoperative X-ray images could be estimated to obtain CT angiography and intraoperative X-ray images. The spatial positional relationship was obtained. Finally, according to the pose parameters of the X-ray image in the CT angiography coordinate system, the thoracic aorta image in the CT angiography was re-projected and superimposed into the intraoperative X-ray image to navigation assistance for the doctors. RESULTSANDCONCLUSION: (1) The experimental results show that the root mean square error of the proposed algorithmis reduced by 17%comparedwiththe traditional algorithmsof gradient correlation and mode strength. (2) In the dual-branch code structure network, the parameter estimation error is reduced to 30% of the network without branching structure in the digital reconstruction image test set. (3) In the experimental X-image experiment, the root mean square error is also reduced by2%.