Image fast reconstruction for sparse view computed tomography with reduced sampling integration time

Abstract

Industrial computed tomography (CT) can detect the internal structure of objects nondestructively. Its scanning and image reconstruction are time-consuming, which limits its application in the fast detection field. In this work, we reduce the sampling integration time in sparse view CT to achieve ultra-fast scanning in only 5 s. However, this ultra-fast scanning method produces more noise and streak artifacts in the reconstructed CT images. The existing reconstruction methods cannot effectively reconstruct high quality CT images under this ultra-fast scanning. To solve this issue, a faster and higher quality CT image reconstruction method is proposed in this paper. We construct a novel objective function using multi-directional TV and adaptive non-local means (ANLM), and design a solution algorithm to solve the objective function. The multi-directional TV-norm takes into account the differences in horizontal, vertical, and 45° directional gradients to obtain more image detail information, while the ANLM-norm focuses on noise suppression. Simulation and practical experiments show that the proposed method can reconstruct higher quality CT images under faster CT scanning strategy, and the peak signal to noise ratio (PSNR) is 1.3 dB higher than the optimal baseline algorithm. On the other hand, our method converges faster and achieves a shorter CT image reconstruction time, which is 47 s shorter than the current popular iterative reconstruction methods. This reconstruction method provides a reliable scheme for faster CT scanning and image reconstruction.