title: Bayesian statistical reconstruction for low-dose X-ray computed tomography using an adaptive-weighting nonlocal prior.
abstract: How to reduce the radiation dose delivered to the patients has always been a important concern since the introduction of computed tomography (CT). Though clinically desired, low-dose CT images can be severely degraded by the excessive quantum noise under extremely low X-ray dose circumstances. Bayesian statistical reconstructions outperform the traditional filtered back-projection (FBP) reconstructions by accurately expressing the system models of physical effects and the statistical character of the measurement data. This work aims to improve the image quality of low-dose CT images using a novel AW nonlocal (adaptive-weighting nonlocal) prior statistical reconstruction approach. Compared to traditional local priors, the proposed prior can adaptively and selectively exploit the global image information. It imposes an effective resolution-preserving and noise-removing regularization for reconstructions. Experimentation validates that the reconstructions using the proposed prior have excellent performance for X-ray CT with low-dose scans.
authors.affiliation: The Laboratory of Image Science and Technology, Southeast University, China.
authors.forename: Yang
authors.initials: Y
authors.lastname: Chen
authors.forename: Dazhi
authors.initials: D
authors.lastname: Gao
authors.forename: Cong
authors.initials: C
authors.lastname: Nie
authors.forename: Limin
authors.initials: L
authors.lastname: Luo
authors.forename: Wufan
authors.initials: W
authors.lastname: Chen
authors.forename: Xindao
authors.initials: X
authors.lastname: Yin
authors.forename: Yazhong
authors.initials: Y
authors.lastname: Lin
mesh_terms: D001499:Bayes Theorem; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D011829:Radiation Dosage; D014057:Tomography, X-Ray Computed