The sparse-view problem of image reconstruction encountered in computed tomography
(CT) is an important research issue due to its considerable potential in decreasing radiation …

Inspired by the compressed sensing (CS) theory, introducing priori information of sparse
image into sparse-view reconstruction algorithm of computed tomography (CT) can improve …

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 …

The sparse view problem of image reconstruction encountered in computed tomography
(CT) is an important research issue due to its considerable potential in lowering radiation …

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 …

Sparse-view CT reconstruction algorithms via total variation (TV) optimize the data iteratively
on the basis of a noise-and artifact-reducing model, resulting in significant radiation dose …

Abstract Background The sparse CT (Computed Tomography), inspired by compressed
sensing, means to introduce a prior information of image sparsity into CT reconstruction to …

Background With the development of low‐dose computed tomography (CT), incomplete data
reconstruction has been widely concerned. The total variation (TV) minimization algorithm …

OBJECTIVE: Although the sinogram data is easily manipulated, it lacks a statistical
description for image reconstruction. To address this problem, we present an alpha …

Because radiation is harmful to patients, it is important to reduce X-ray exposure in the clinic.
For CT, reconstructions from sparse views or limited angle tomography are being used more …