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.

In recent years, deep learning has shown outstanding performance and potential in pattern recognition and feature extraction, which has attracted an increasing amount of attention from engineering researchers and academics. Fault diagnosis methods based on deep learning have also become the focus of a significant amount of research. In this paper, a nonlinear process fault diagnosis and identification method based on DBN-dropout is proposed. The deep belief network (DBN) has significant advantages in dealing with nonlinear processes, and it can extract the abstract representation of nonlinear process data to build a deep network to achieve the realtime monitoring of process operations. Dropout technology can reduce overfitting and improve the generalization ability of the model. Afterwards, the Tennessee Eastman (TE) process is employed to analyze the performance of the proposed approach. K E Y W O R D S deep belief network, dropout, fault diagnosis, Tennessee Eastman process

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