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To overcome the oversmoothing drawback in the edge areas when reconstructing few-view CT with total variation (TV) minimization, in this paper, we propose an anisotropic edge-guided TV minimization framework for few-view CT reconstruction. In the framework, anisotropic TV is summed with pre-weighted image gradient and then used as the object function for minimizing. It includes edge-guided TV minimization...
To improve the spatial resolution of the image reconstructed by the conventional total variation (TV) algorithm, we propose a prior image based anisotropic edge guided TV minimization (PIEGTV) algorithm for few-view CT reconstruction. In this study, an anisotropic edge of the prior image is detected using the proposed edge detector. Then the weights of the TV discretization term for the to-be-estimated...
To further alleviate the ionizing radiation damage of computed tomography (CT), we propose a method of sparse-view reconstruction based on low-dose CT projection data. It first utilizes a penalized weighted least square (PWLS) restoration for low-dose CT projection based on noise modeling. Then the CT images are reconstructed from fewer views of denoised projection data. Reconstruction results from...
Based on compressed sensing (CS) recovery theory, total variation (TV) minimization has been successfully used in computed tomography (CT) reconstruction for sparse or limited angle data. When the number of projection views is much smaller or noise exists in the projection data, a conventional TV minimization algorithm often suffers from the decrease of spatial resolution especially in the edge area...
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