The goal of this paper is to develop a new architecture for industrial computed tomography (ICT) aiming at dynamically imaging an aperiodic changing object. We propose a data acquisition approach with multiple x-ray source/detector pairs targeting a continuously changeable object with corresponding timeframes. In this named swinging multi-source CT (SMCT) structure, each source and its associated detector swing forth and back within a certain angle for CT scanning. In the SMCT system design, we utilize a circular journal bearing based setup to replace the normal CT slip ring by weakening the scanning speed requirement. Inspired by the prior image constrained compressed sensing (PICCS) algorithm, we apply a modified PICCS algorithm for the SMCT (SM-PICCS). Our numerical simulation and realistic specimen experiment studies demonstrate the feasibility of the proposed approach.

Spectral-computed tomography (CT) has been demonstrating its great advantages in lesion detection, tissue characterization, and material decomposition. However, the quality of images is often significantly corrupted with various noises, which brings a great challenge for its applications. Because the channel-wise images from different energy interval share similar structure and physical message, the spatial sparsity, global correlation across the spectrum (GCS), and nonlocal self-similarity (NSS) as three important characteristics are employed to spectral CT reconstruction. In this study, we propose a spectral-image decomposition with energy-fusion sensing (SIDES) reconstruction method, which encourages to obtain better quality spectral images and material decomposition results by establishing a unified tensor decomposition model. First, considering the noise distribution in channel-wise and the difference of linear attenuation coefficients within channel-cross, an adaptive weighted full-spectrum prior image as additional supervised information is incorporated to formulate a new weighted prior image-based tensor. Cooperating with original image tensor, they fully explore the spatial sparsity, GCS, and NSS properties. Then, we formulate nonlocal similar patch-based tensor groups to encode the NSS property from image-domain and residual-image-domain (which is expanded by prior-image and image-self). Next, low-rank regularized Tucker tensor decomposition is employed to fully explore the intrinsic knowledge with the help of prior-image supervision. Finally, the relaxed convex optimization model is optimized by dividing reconstruction model into several subproblem using split-Bregman method. Numerical simulations and real experiments are designed to validate and evaluate the SIDES method and the results demonstrate that the SIDES reconstruction outperforms the state-of-the-art.

In some computed tomography (CT) applications, limited-angle projections are used for image reconstruction, and traditional reconstruction methods, such as filtered back-projection (FBP) or simultaneous algebraic reconstruction technique (SART), cannot reconstruct high-quality CT images without prior knowledge assistance. For limited-angle CT reconstruction, total variation minimization (TVM) method is not conductive to recovering image structures. Image reconstruction methods based on anisotropic total variation (ATV) and reweighted anisotropic total variation (RwATV) can significantly reduce the shading artifacts using prior knowledge of the scanning angular range and image sparsity. However, using the knowledge of image sparsity does not further improve image quality in some applications. In this paper, we propose a new reconstruction method based on anisotropic relative total variation (ARTV) for limited-angle CT reconstruction. In ARTV, the windowed inherent variation (WIV) indicates the strength of structure information and WIV values are adaptively determined by local structure information. In limited-angle CT, particular scanning angular range urges us into exerting different strengths on different directions of ARTV. Experiments on FORBILD HEAD phantom, a thoracic image and real projections of a walnut are performed to test the new method. Experimental results show that the destroyed structures are recovered to some extent, and we acquire higher image quality compared to some existing limited-angle CT reconstruction methods.