title: CT Image Reconstruction from Sparse Projections Using Adaptive TpV Regularization.
abstract: Radiation dose reduction without losing CT image quality has been an increasing concern. Reducing the number of X-ray projections to reconstruct CT images, which is also called sparse-projection reconstruction, can potentially avoid excessive dose delivered to patients in CT examination. To overcome the disadvantages of total variation (TV) minimization method, in this work we introduce a novel adaptive TpV regularization into sparse-projection image reconstruction and use FISTA technique to accelerate iterative convergence. The numerical experiments demonstrate that the proposed method suppresses noise and artifacts more efficiently, and preserves structure information better than other existing reconstruction methods.
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
authors.forename: Hongliang
authors.identifier: 0000-0002-2764-0898
authors.initials: H
authors.lastname: Qi
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
authors.forename: Zijia
authors.initials: Z
authors.lastname: Chen
authors.affiliation: School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
authors.forename: Linghong
authors.initials: L
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D006801:Humans; D015233:Models, Statistical; D019047:Phantoms, Imaging; D011829:Radiation Dosage; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D014057:Tomography, X-Ray Computed / Q000706:statistics & numerical data*