Radiation dose reduction in pediatric great vessel stent computed tomography using iterative reconstruction: A phantom study

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.

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

To further reduce the noise and artifacts in the reconstructed image of sparse-view CT, we have modified the traditional total variation (TV) methods, which only calculate the gradient variations in x and y directions, and have proposed 8- and 26-directional (the multi-directional) gradient operators for TV calculation to improve the quality of reconstructed images. Different from traditional TV methods, the proposed 8- and 26-directional gradient operators additionally consider the diagonal directions in TV calculation. The proposed method preserves more information from original tomographic data in the step of gradient transform to obtain better reconstruction image qualities. Our algorithms were tested using two-dimensional Shepp–Logan phantom and three-dimensional clinical CT images. Results were evaluated using the root-mean-square error (RMSE), peak signal-to-noise ratio (PSNR), and universal quality index (UQI). All the experiment results show that the sparse-view CT images reconstructed using the proposed 8- and 26-directional gradient operators are superior to those reconstructed by traditional TV methods. Qualitative and quantitative analyses indicate that the more number of directions that the gradient operator has, the better images can be reconstructed. The 8- and 26-directional gradient operators we proposed have better capability to reduce noise and artifacts than traditional TV methods, and they are applicable to be applied to and combined with existing CT reconstruction algorithms derived from CS theory to produce better image quality in sparse-view reconstruction.