Bayesian statistical reconstruction for low-dose X-ray computed tomography using an adaptive-weighting nonlocal prior.
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.
Chen
Yang
Y
The Laboratory of Image Science and Technology, Southeast University, China.
Gao
Dazhi
D
Nie
Cong
C
Luo
Limin
L
Chen
Wufan
W
Yin
Xindao
X
Lin
Yazhong
Y
D001499:Bayes Theorem; D006801:Humans; D008390:Markov Chains; D015233:Models, Statistical; D011829:Radiation Dosage; D014057:Tomography, X-Ray Computed