Limited-view CT Reconstruction Based on Autoencoder-like Generative Adversarial Networks with Joint Loss

Limiting the scan views of X-ray computed tomography (CT) can make radiation dose reduced efficiently and consequently weaken the damage of ionizing radiation. However, it will degrade the reconstructed CT images. In this paper, we proposed to predict the missing projections and improve the reconstructed CT images by constructing an autoencoder-like generative adversarial network (GAN) with joint loss function. In the generator network, we train an autoencoder-like convolutional neural network (CNN) to generate the missing projections given a sinogram of the limited-view CT projections. For the discriminator network, a CNN is used to classify an input sinogram as real or synthetic one. To produce more realistic images, the joint loss function which includes not only reconstruction loss, but the adversarial loss is employed. While reconstruction loss can capture the overall structure of the missing projections, the latter can pick a particular mode from the distribution and make the results much sharper. After the missing projections have been estimated, we reconstruct the CT images from the completed projections by utilizing conventional filtered back-projection (FBP) method. The experiments prove the capability of our method to achieve a considerable improvement in limited-view CT reconstruction.