Low-dose CT statistical iterative reconstruction via modified MRF regularization. Issue 123 (January 2016)

• Record Type: Journal Article
• Authors: Shangguan, Hong; Zhang, Quan; Liu, Yi; Cui, Xueying; Bai, Yunjiao; Gui, Zhiguo
• Is Part Of: Computer methods and programs in biomedicine. Issue 123(2016)
• Page Start: 129
• Page End: 141
• Publication Date: 2016-01
• Subjects: CT -- Statistical iterative reconstruction -- Markov random field -- Total generalized variation -- Regularization; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2015.10.004
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Proposed a novel regularization scheme MMRF for CT reconstruction. Present a new objective function and deduce its iterative equation. Optimize the objective function by a modified alternative iterative algorithm. Design experiments on different phantoms to validate the validity of the approach. Analyze the results by visual evaluation and quantitative evaluation. Abstract: It is desirable to reduce the excessive radiation exposure to patients in repeated medical CT applications. One of the most effective ways is to reduce the X-ray tube current (mAs) or tube voltage (kVp). However, it is difficult to achieve accurate reconstruction from the noisy measurements. Compared with the conventional filtered back-projection (FBP) algorithm leading to the excessive noise in the reconstructed images, the approaches using statistical iterative reconstruction (SIR) with low mAs show greater image quality. To eliminate the undesired artifacts and improve reconstruction quality, we proposed, in this work, an improved SIR algorithm for low-dose CT reconstruction, constrained by a modified Markov random field (MRF) regularization. Specifically, the edge-preserving total generalized variation (TGV), which is a generalization of total variation (TV) and can measure image characteristics up to a certain degree of differentiation, was introduced to modify the MRF regularization. In addition, a modified alternating iterative algorithm was utilized to optimize the cost function. Experimental results demonstrated that images reconstructed by the proposed method could not only generate high accuracy and resolution properties, but also ensure a higher peak signal-to-noise ratio (PSNR) in comparison with those using existing methods.