  Many CT slice images are stored with large slice intervals to reduce storage
size in clinical practice. This leads to low resolution perpendicular to the
slice images (i.e., z-axis), which is insufficient for 3D visualization or
image analysis. In this paper, we present a novel architecture based on
conditional Generative Adversarial Networks (cGANs) with the goal of generating
high resolution images of main body parts including head, chest, abdomen and
legs. However, GANs are known to have a difficulty with generating a diversity
of patterns due to a phenomena known as mode collapse. To overcome the lack of
generated pattern variety, we propose to condition the discriminator on the
different body parts. Furthermore, our generator networks are extended to be
three dimensional fully convolutional neural networks, allowing for the
generation of high resolution images from arbitrary fields of view. In our
verification tests, we show that the proposed method obtains the best scores by
PSNR/SSIM metrics and Visual Turing Test, allowing for accurate reproduction of
the principle anatomy in high resolution. We expect that the proposed method
contribute to effective utilization of the existing vast amounts of thick CT
images stored in hospitals.
