Performance of sparse-view CT reconstruction with multi-directional gradient operators

Reconstruction of Sparse-View X-ray Computed Tomography Based on Adaptive Total Variation Minimization

Sparse-view reconstruction has garnered significant interest in X-ray computed tomography (CT) imaging owing to its ability to lower radiation doses and enhance detection efficiency. Among current methods for sparse-view CT reconstruction, an algorithm utilizing iterative reconstruction based on full variational regularization demonstrates good performance. The optimized direction and number of computations for the gradient operator of the regularization term play a crucial role in determining not only the reconstructed image quality but also the convergence speed of the iteration process. The conventional TV approach solely accounts for the vertical and horizontal directions of the two-dimensional plane in the gradient direction. When projection data decrease, the edges of the reconstructed image become blurred. Exploring too many gradient directions for TV terms often comes at the expense of more computational costs. To enhance the balance of computational cost and reconstruction quality, this study suggests a novel TV computation model that is founded on a four-direction gradient operator. In addition, selecting appropriate iteration parameters significantly impacts the quality of the reconstructed image. We propose a nonparametric control method utilizing the improved TV approach as a solution to the tedious manual parameter optimization issue. The relaxation parameters of projection onto convex sets (POCS) are determined according to the scanning number and numerical proportion of the projection data; according to the image error before and after iterations, the gradient descent step of the TV item is adaptively adjusted. Compared with several representative iterative reconstruction algorithms, the experimental results show that the algorithm can effectively preserve edges and suppress noise in sparse-view CT reconstruction.

CT Image Reconstruction using NLMfuzzyCD Regularization Method

Aims and scope: Computed Tomography (CT) is one of the most efficient clinical diagnostic tools. The main goal of CT is to reproduce an acceptable reconstructed image of an object (either anatomical or functional behaviour) with the help of a limited set of its projections at different angles.

BACKGROUND

To achieve this goal, one of the most commonly iterative reconstruction algorithm called Maximum Likelihood Expectation Maximization (MLEM) is used.

OBJECTIVE

Although the conventional Maximum Likelihood (ML) algorithm can achieve quality images in CT. However, it still suffers from the optimal smoothing as the number of iterations increase.

METHODS

For solving this problem, in this paper present a novel statistical image reconstruction algorithm for CT, which utilizes a nonlocal means fuzzy complex diffusion as a regularization term for noise reduction and edge preservation.

RESULTS

The proposed model was evaluated on four test cases phantoms.

CONCLUSION

Qualitative and quantitative analyses indicate that the proposed technique has higher efficiency for computed tomography. The proposed method yields significant improvements when compare with the state-of-the-art techniques.