  Computed tomography (CT) can provide a 3D view of the patient's internal
organs, facilitating disease diagnosis, but it incurs more radiation dose to a
patient and a CT scanner is much more cost prohibitive than an X-ray machine
too. Traditional CT reconstruction methods require hundreds of X-ray
projections through a full rotational scan of the body, which cannot be
performed on a typical X-ray machine. In this work, we propose to reconstruct
CT from two orthogonal X-rays using the generative adversarial network (GAN)
framework. A specially designed generator network is exploited to increase data
dimension from 2D (X-rays) to 3D (CT), which is not addressed in previous
research of GAN. A novel feature fusion method is proposed to combine
information from two X-rays.The mean squared error (MSE) loss and adversarial
loss are combined to train the generator, resulting in a high-quality CT volume
both visually and quantitatively. Extensive experiments on a publicly available
chest CT dataset demonstrate the effectiveness of the proposed method. It could
be a nice enhancement of a low-cost X-ray machine to provide physicians a
CT-like 3D volume in several niche applications.
