Abstract
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.
Highlights
IntroductionStudies related to X-ray computed tomography (CT) have flourished in the past three decades [8,9,10,11,12,13,14,15]
Since the mathematical model of image reconstruction was proposed by Radon in 1917 and the X-ray computed tomography (CT) scanner was invented by Hounsfield in 1972 [1,2,3], CT technology has been widely employed in various clinical institutions because it is noninvasive
The reconstruction results after six iterations are presented in Figs 3 and 4, and the quantitative analysis results are displayed in Tables 1 and 2
Summary
Studies related to X-ray CT have flourished in the past three decades [8,9,10,11,12,13,14,15]. Because of the arising in health awareness, an increasing number of people have become concerned about the radiation dose in using X-ray CT [16,17,18,19]. Studies have reported that an excessive X-ray dose increases the risk of tissue diseases and cancers [20,21,22,23]. How to reduce X-ray dose in CT while maintaining image quality has been a highly active research topic in the past decades [24,25,26,27]
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