  X-ray images play a vital role in the intraoperative processes due to their
high resolution and fast imaging speed and greatly promote the subsequent
segmentation, registration and reconstruction. However, over-dosed X-rays
superimpose potential risks to human health to some extent. Data-driven
algorithms from volume scans to X-ray images are restricted by the scarcity of
paired X-ray and volume data. Existing methods are mainly realized by modelling
the whole X-ray imaging procedure. In this study, we propose a learning-based
approach termed CT2X-GAN to synthesize the X-ray images in an end-to-end manner
using the content and style disentanglement from three different image domains.
Our method decouples the anatomical structure information from CT scans and
style information from unpaired real X-ray images/ digital reconstructed
radiography (DRR) images via a series of decoupling encoders. Additionally, we
introduce a novel consistency regularization term to improve the stylistic
resemblance between synthesized X-ray images and real X-ray images. Meanwhile,
we also impose a supervised process by computing the similarity of computed
real DRR and synthesized DRR images. We further develop a pose attention module
to fully strengthen the comprehensive information in the decoupled content code
from CT scans, facilitating high-quality multi-view image synthesis in the
lower 2D space. Extensive experiments were conducted on the publicly available
CTSpine1K dataset and achieved 97.8350, 0.0842 and 3.0938 in terms of FID, KID
and defined user-scored X-ray similarity, respectively. In comparison with
3D-aware methods ($\pi$-GAN, EG3D), CT2X-GAN is superior in improving the
synthesis quality and realistic to the real X-ray images.
