Multiple limited-angles computed tomography reconstruction based on multi-direction total variation minimization

Orthogonal limited-angle CT reconstruction method based on anisotropic self-guided image filtering

Computed tomography (CT) reconstruction from incomplete projection data is significant for reducing radiation dose or scanning time. In this work, we investigate a special sampling strategy, which performs two limited-angle scans. We call it orthogonal limited-angle sampling. The X-ray source trajectory covers two limited-angle ranges, and the angle bisectors of the two angular ranges are orthogonal. This sampling method avoids rapid switching of tube voltage in few-view sampling, and reduces data correlation of projections in limited-angle sampling. It has the potential to become a practical imaging strategy. Then we propose a new reconstruction model based on anisotropic self-guided image filtering (ASGIF) and present an algorithm to solve this model. We construct adaptive weights to guide image reconstruction using the gradient information of reconstructed image itself. Additionally, since the shading artifacts are related to the scanning angular ranges and distributed in two orthogonal directions, anisotropic image filtering is used to preserve image edges. Experiments on a digital phantom and real CT data demonstrate that ASGIF method can effectively suppress shading artifacts and preserve image edges, outperforming other competing methods.

Image reconstruction method for incomplete CT projection based on self-guided image filtering

In some fields of medical diagnosis or industrial nondestructive testing, it is difficult to obtain complete computed tomography (CT) data due to the limitation of radiation dose or other factors. Therefore, image reconstruction of incomplete projection data is the focus of this paper. In this paper, a new image reconstruction model based on self-guided image filtering (SGIF) term is proposed for few-view and segmental limited-angle (SLA) CT reconstruction. Then the alternating direction method (ADM) is used to solve this model. For simplicity, we call it ADM-SGIF method. The key idea of ADM-SGIF method is to use the reconstructed image itself as a reference and utilize its structural features to guide CT reconstruction. This method can effectively preserve image structures and remove shading artifacts. To validate the effectiveness of the proposed reconstruction method, we conduct digital phantom and real CT data experiments. The results indicate that ADM-SGIF method outperforms competing methods, including total variation (TV), relative total variation (RTV), and L0-norm minimization solved by ADM (ADM-L0) methods, in both subjective and objective evaluations.

Segmental limited-angle CT reconstruction based on image structural prior

CT reconstruction from incomplete projection data is one of the key researches of X-ray CT imaging. The projection data acquired by few-view and limited-angle sampling are incomplete. In addition, few-view sampling often requires turning on and off the tube voltage, but rapid switching of tube voltage demands for high technical requirements. Limited-angle sampling is easy to realize. However, reconstructed images may encounter obvious artifacts. In this study we investigate a new segmental limited-angle (SLA) sampling strategy, which avoids rapid switching of tube voltage. Thus, the projection data has lower data correlation than limited-angle CT, which is conducive to reconstructing high-quality images. To suppress potential artifacts, we incorporate image structural prior into reconstruction model to present a reconstruction method. The limited-angle CT reconstruction experiments on digital phantoms, real carved cheese and walnut projections are used to test and verify the effectiveness of the proposed method. Several image quality evaluation indices including RMSE, PSNR, and SSIM of the reconstructions in simulation experiments are calculated and listed to show the superiority of our method. The experimental results indicate that the CT image reconstructed using the proposed new method is closer to the reference image. Images from real CT data and their residual images also show that applying the proposed new method can more effectively reduce artifacts and image structures are well preserved.

Exterior computed tomography image reconstruction based on anisotropic relative total variation in polar coordinates

In computed tomography (CT) image reconstruction problems, exterior CT is an important application in industrial non-destructive testing (NDT). Different from the limited-angle problem that misses part of the rotation angle, the rotation angle of the exterior problem is complete, but for each rotation angle, the projection data through the central region of the object cannot be collected, so that the exterior CT problem is ill-posed inverse problem. The results of traditional reconstruction methods like filtered back-projection (FBP) and simultaneous algebra reconstruction technique (SART) have artifacts along the radial direction edges for exterior CT reconstruction. In this study, we propose and test an anisotropic relative total variation in polar coordinates (P-ARTV) model for addressing the exterior CT problem. Since relative total variation (RTV) can effectively distinguish edges from noises, and P-ARTV with different weights in radial and tangential directions can effectively enhance radial edges, a two-step iteration algorithm was developed to solve the P-ARTV model in this study. The fidelity term and the regularization term are solved in Cartesian and polar coordinate systems, respectively. Numerical experiments show that our new model yields better performance than the existing state-of-the-art algorithms.