TPG-rayGAN: CT reconstruction based on transformer and generative adversarial networks

Computed tomography (CT) provides a three-dimensional view of the patient’s internal organs. X-ray imaging offers a two-dimensional view for patients. X-ray images are more commonly available and less costly than CT, and the radiation dose to the patient is significantly reduced. Traditional CT imaging methods require projection with hundreds of X-rays for a full body scan. An end-to-end generative adversarial networks (GAN) network approach, i.e., TPG-rayGAN, was proposed for reconstructing lung CT volumes directly from biplane X-ray images. In this work, CT was reconstructed with ultra-low radiation. Densely connected networks and Transformer networks were connected in parallel to extract features. In addition, the perceptual loss function was added in the loss function section. The experimental results show that high-quality CT can be reconstructed from X-ray images using TPG-rayGAN.