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When testing small size and high density objects with a broad field-of-view (FOV) industrial CT system, multiple objects are always assembled evenly onto the turntable for detecting to improve test efficiency. However, the maximum X-ray's penetrating path through the materials increases, which means the CT system should collocate with a high energy X-ray source and wide-dynamic range detectors to complete the CT scanning. In this study, we proposed and tested a novel and efficient CT scanning method based on linear-arrangement and synchronous-rotating multi-turntables without enhancing the energy of X-ray source and wide-dynamic range of detectors for the CT system. With this modality, multiple objects are assembled onto multiple synchronous-rotating turntables respectively, and X-rays within the FOV merely penetrate one single object when scanning. The corresponding filtered back projection algorithm for image reconstruction is deduced. The computer simulation and experimental results verified the feasibility of this novel method and the scanning time was reduced to 5-8 minutes when completing the scanning of 3 to 5 group objects.

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To reduce artifacts of CT image reconstructed from limited-angle projections data, we develop a biregular term optimization algorithm, which introduces the singular value decomposition (SVD) as an additional regularization term on the basis of gradient L0 norm regularization. We combine the alternating direction (ADM) method and the variable splitting method to solve the proposed optimization model. Firstly, the regularization of gradient L0 norm is performed, which takes advantage of the sparsity of the image gradient. Then, the regularization of SVD is carried out, which based on the low rank of the image. Finally, the weighted errors of two regularized image are fed back to the iterative reconstruction process. The experimental results show that, compared with the SART, SART+SVD,SART+L0 algorithms, the maximum peak signal-to-noise ratio (PSNR) of the proposed algorithm is increased by about 28%, 25%, 1%, and the root mean square error (RMSE) is reduced by about 11%, 8%, 0.05%, respectively. The proposed biregular term optimization algorithm can accurately restore image edge, effectively reduce the artifacts reconstructed by limited-angle projections, and significantly improve image quality.

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