UDCR: Unsupervised Aortic DSA/CTA Rigid Registration Using Deep
  Reinforcement Learning and Overlap Degree Calculation
Wentao Liu Bowen Liang Weijin Xu Tong Tian Qingsheng Lu Xipeng Pan Haoyuan Li Siyu Tian Huihua Yang Ruisheng Su
The rigid registration of aortic Digital Subtraction Angiography (DSA) and
Computed Tomography Angiography (CTA) can provide 3D anatomical details of the
vasculature for the interventional surgical treatment of conditions such as
aortic dissection and aortic aneurysms, holding significant value for clinical
research. However, the current methods for 2D/3D image registration are
dependent on manual annotations or synthetic data, as well as the extraction of
landmarks, which is not suitable for cross-modal registration of aortic
DSA/CTA. In this paper, we propose an unsupervised method, UDCR, for aortic
DSA/CTA rigid registration based on deep reinforcement learning. Leveraging the
imaging principles and characteristics of DSA and CTA, we have constructed a
cross-dimensional registration environment based on spatial transformations.
Specifically, we propose an overlap degree calculation reward function that
measures the intensity difference between the foreground and background, aimed
at assessing the accuracy of registration between segmentation maps and DSA
images. This method is highly flexible, allowing for the loading of pre-trained
models to perform registration directly or to seek the optimal spatial
transformation parameters through online learning. We manually annotated 61
pairs of aortic DSA/CTA for algorithm evaluation. The results indicate that the
proposed UDCR achieved a Mean Absolute Error (MAE) of 2.85 mm in translation
and 4.35{\deg} in rotation, showing significant potential for clinical
applications.