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With the fast development of photon counting detection techniques, spectral computed tomography (CT) with a photon counting detector has attracted considerable attention by increasing energy resolution to identify and discriminate materials. The conventional analytic reconstruction algorithms can be directly applied to reconstruct spectral CT images for each spectrum or energy bin. However, a comprehensive evaluation of analytic reconstruction algorithms for spectral CT has not been reported yet. This motivates us to evaluate the analytic spiral cone-beam CT algorithms and to provide a fair comparison platform for the state-of-the-art iterative spectral CT reconstruction algorithms. Considering the fact that a narrow energy bin has high noise which degrades the imaging quality of spectral CT, an adaptive maximum a posterior projection restoration algorithm is first used to reduce the noise, and then, a 2-D/3-D weighted spiral Feldkamp–Davis–Kress algorithm is implemented to reconstruct the spectral CT images at different energy bins. Finally, the principle component analysis is employed to render the spectral reconstruction results into a color space. Our numerical results show that the analytic reconstruction approach is fast and it can provide high spatial resolution, high contrast resolution, and high signal-to-noise-ratio under higher helical pitches. This makes it possible to serve as a platform to evaluate the state-of-the-art iterative spectral CT algorithms.

In the process of X-ray digital imaging detection, previous test usually adopts the projection data of 360° to reconstruct. But in practical test, it's often restricted to the installation of the equipment and the cost, so it cannot meet the condition of reconstruction. This paper studies a method to solve the coordinate of circle center about a particular component by some experience information, which is based on the projection values of three views in order to determine the distribution of holes. Experiment shows that the method not only reduces the collection angles and the cost, which satisfies the problem of detection in practical application, but also guarantees the accuracy of the positional information of the internal holes, which is compared with the 900 views under reconstruction.

Fixed-voltage Digital Radiography(DR) image has over-exposed or under-exposed regions since dynamic range of the imaging device is not enough when testing a composite component with a complicated internal structure. Some structure information of the composite component is lost. This problem can be solved by fusing the multi-voltage DR images. A multi-voltage DR images fusion model is proposed based on the gray consistency of the images. It adaptively determines the weights of all images by solving the model. What's more, this method is without prior knowledge. The experiment shows the new method can reconstruct the whole information of the complicated structural component. Also the new method has higher fusion efficiency because of no prior knowledge.

A method that can fast, real-time get the projection matrix in ART reconstruction is proposed based on helical cone-beam CT trajectory in this paper. For a given ray this method can rapidly acquire the indices and corresponding lengths of the voxels passed through by this ray through sorting the intersections between the ray and mesh planes. Meanwhile we only store the projection matrix elements required for a given ray and release them after using them. By doing so the computation time and the storage space are significantly reduced. The experimental results show that this method can heighten the speed and better suppress noise.

Target localization based on time difference of arrival (TDOA) measurements has important applications in sonar, radar and sensor networks. This paper simply introduced the target localization principle of moving emitter and the position location algorithm. Further more presented an improved particle swarm optimization with stochastic inertia weight and adaptive mutation, and adopts it to solve the target localization problem according to the batch of continuous TDOA measurements. The experimental results show that the new algorithm has higher localization accuracy, better algorithm stability and faster convergence rate.

Ordered-subset simultaneous algebraic reconstruction technique (OS-SART) was studied by Ge Wang and Ming Jiang in 2004. It accelerate the convergence of SART, but it has some disadvantages, such as increasing the number of subsets accelerates iterative convergence, but there is a point beyond which image quality degrades due to a lack of statistical information within subset. In this paper, a new method of subset partition based on statistical test is proposed as an improved OS- SART (IOS-SART). IOS-SART can automatically adjust the number of the subsets for each iteration according to the statistical information content within subset demanded by user. Numerical simulation and application to practical data demonstrate that this algorithm converge faster and can provide high quality reconstructed images after a small number of iterations.