Recently a new Thomson scattering diagnostic system was upgraded in EAST tokamak experiment using a multipulse Nd:YAG (neodymium-yttrium aluminium garnet) laser and a multipoint observation volumes. This diagnostic uses a new optical laser alignment technique that was made to determine accurately the laser position, and a new lens collection system that enables the measurement of wider plasma's object. A composite control system made we can get the results in several seconds. Furthermore, a new data processing method was adopted for much exact results.

A multipulse and multipoint Nd:YAG (neodymium-yttrium aluminum garnet) laser Thomson scattering diagnostic system was developed on EAST to obtain more accurate electron temperature Te and electron density ne profiles. In this paper, the optical system, the VME (versa module eurocard)-based real-time computer system for laser control, data acquisition, analysis and calibration are discussed in detail. Furthermore, a developed data processing method is presented.

In geometric calibration of cone-beam computed tomography (CBCT), sphere-like objects such as balls are widely imaged, the positioning information of which is obtained to determine the unknown geometric parameters. In this process, the accuracy of the detector location of CB projection of the center of the ball, which we call the center projection, is very important, since geometric calibration is sensitive to errors in the positioning information. Currently in almost all the geometric calibration using balls, the center projection is invariably estimated by the center of the support of the projection or the centroid of the intensity values inside the support approximately. Clackdoyle's work indicates that the center projection is not always at the center of the support or the centroid of the intensity values inside, and has given a quantitative analysis of the maximum errors in evaluating the center projection by the centroid. In this paper, an exact method is proposed to calculate the center projection, utilizing both the detector location of the ellipse center and the two axis lengths of the ellipse. Numerical simulation results have demonstrated the precision and the robustness of the proposed method. Finally there are some comments on this work with non-uniform density balls, as well as the effect by the error occurred in the evaluation for the location of the orthogonal projection of the cone vertex onto the detector.

Metal artifacts seriously degrade the quality of the CT data and bring great difficulties to subsequent image processing and analysis, which nowadays become a great concern in X-ray CT applications. In this paper, we introduce a U-net-based metal artifact reduction method into CT image domain. The proposed network reduces metal artifacts by learning an end-to-end mapping of images from metal-corrupted CT images to their corresponding artifact-free ground truth images. We design and optimize the network through the simulation experiments. The experimental results show that the proposed method can well reduce metal artifacts of CT images, and this method has higher computational efficiency and greatly shortens the processing time. It avoids complex image preprocessing and can accept input images of any size. Therefore, it can be a more automated way to handle large amounts of data, making it ideal for existing CT workflows.