Limited-View Cone-Beam CT Reconstruction Based on an Adversarial Autoencoder Network With Joint Loss

Abstract

Limiting scan views is an efficient way to reduce radiation doses in the cone-beam computed tomography (CBCT) examinations, which unfortunately degrades the reconstructed images. Some methods on the framework of the generative adversarial network (GAN) were developed to improve low-dose CT images after CT reconstruction from the limited-view projections. However, no GAN-based methods were devoted to restoring missing CBCT projections in the sinogram domain before CT reconstruction. To avoid the trade-off between radiation dose and image quality, we propose a limited-view CBCT reconstruction method in the sinogram domain, instead of the image domain. First, this method slices the 3D CBCT projections into multiple 2D pieces. Then, an adversarial autoencoder network is trained to estimate the missing parts of these 2D pieces. To improve the prediction, we apply a joint loss function, including reconstruction loss and adversarial loss to the network. When the new limited-view 3D CBCT projections are acquired, the proposed method uses the trained adversarial autoencoder network to generate the missing parts of the 2D pieces sliced from the current 3D CBCT projections. Then, stacking the completed 2D pieces in order yields full-view 3D CBCT projections. Finally, we reconstruct the CT images from the full-view 3D CBCT projections by using the Feldkamp, Davis, and Kress algorithm. The experiments validate that our method performs well in the prediction of unknown projections and CT reconstruction and are less vulnerable to the number of unknown projections than other methods.