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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.INDEX TERMS Limited-view CBCT reconstruction, adversarial autoencoder network, reduction of radiation doses, prediction of missing CBCT projections.

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Device-to-device (D2D) communications provide efficient ways to increase spectrum utilization ratio with reduced power consumption for proximity wireless applications. In this paper, we investigate resource allocation strategies for D2D communications underlaying cellular networks. To be specific, we study the centralized resource allocation algorithm for controlling transmit powers of the underlying D2D pairs in order to maximize the weighted sum-rate while guaranteeing the quality of service (QoS) requirements for both D2D pairs and cellular users (CUs). A novel DC (difference of convex function) programming-based method, called alternative DC (ADC) programming, is introduced to effectively solve this complicated resource allocation problem. Through updating each D2D pair's power alternatively, the QoS requirement for each D2D pair can be solvable and incorporated systematically to the introduced ADC programming framework. The simulation results show that the introduced ADC programming achieves the highest weighted sum-rate compared to the state-of-the-art methods while ensuring that the QoS of each D2D pair and CU are satisfied.

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