A nonlinear total variation based computed tomography (CT) image reconstruction method using gradient reinforcement.

ABSTRACT: Compressed sensing-based reconstruction algorithms have been proven to be more successful than analytical or iterative methods for sparse computed tomography (CT) imaging by narrowing down the solution set thanks to its ability to seek a sparser solution. Total variation (TV), one of the most popular sparsifiers, exploits spatial continuity of features by restricting variation between two neighboring pixels in each direction as using partial derivatives. When the number of projections is much fewer than the one in conventional CT, which results in much less sampling rate than the minimum required one, TV may not provide satisfactory results. In this study, a new regularizer is proposed which seeks for a sparser solution by reinforcing the gradient of TV and empowering the spatial continuit

SUBMITTER: Ertas M 

PROVIDER: S-EPMC10782945 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature