title: Statistical iterative reconstruction using adaptive fractional order regularization.
abstract: In order to reduce the radiation dose of the X-ray computed tomography (CT), low-dose CT has drawn much attention in both clinical and industrial fields. A fractional order model based on statistical iterative reconstruction framework was proposed in this study. To further enhance the performance of the proposed model, an adaptive order selection strategy, determining the fractional order pixel-by-pixel, was given. Experiments, including numerical and clinical cases, illustrated better results than several existing methods, especially, in structure and texture preservation.
authors.affiliation: College of Computer Science, Sichuan University, Chengdu 610065, China.
authors.forename: Yi
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: College of Computer Science, Sichuan University, Chengdu 610065, China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Wang
authors.affiliation: College of Computer Science, Sichuan University, Chengdu 610065, China.
authors.forename: Weihua
authors.initials: W
authors.lastname: Zhang
authors.affiliation: College of Computer Science, Sichuan University, Chengdu 610065, China.
authors.forename: Feng
authors.initials: F
authors.lastname: Lin
authors.affiliation: College of Computer Science, Sichuan University, Chengdu 610065, China.
authors.forename: Yifei
authors.initials: Y
authors.lastname: Pu
authors.affiliation: College of Computer Science, Sichuan University, Chengdu 610065, China.
authors.forename: Jiliu
authors.initials: J
authors.lastname: Zhou
keywords: (100.6950) Tomographic image processing; (110.7440) X-ray imaging; (170.3880) Medical and biological imaging