Probabilistic atlas prior for CT image reconstruction. Issue 128 (May 2016)

• Record Type: Journal Article
• Authors: Rashed, Essam A.; Kudo, Hiroyuki
• Is Part Of: Computer methods and programs in biomedicine. Issue 128(2016)
• Page Start: 119
• Page End: 136
• Publication Date: 2016-05
• Subjects: Computed tomography -- Statistical image reconstruction -- Probabilistic atlas -- Laplacian mixture model; 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.2016.02.017
• Languages: English
• ISSNs: 0169-2607

Abstract

Abstract : Highlights: A powerful statistical image reconstruction algorithm for CT is proposed. Data obtained from earlier scans are used to construct a probabilistic atlas with Laplacian mixture model. Prior information obtained from a probabilistic atlas is modeled for the CT image reconstruction. We consider low-dose CT imaging setups using proposed method and alternative approaches. The proposed method outperforms other alternative methods in terms of image quality. Abstract: Background and objectives: In computed tomography (CT), statistical iterative reconstruction (SIR) approaches can produce images of higher quality compared to the conventional analytical methods such as filtered backprojection (FBP) algorithm. Effective noise modeling and possibilities to incorporate priors in the image reconstruction problem are the main advantages that lead to continuous development of SIR methods. Oriented by low-dose CT requirements, several methods are recently developed to obtain a high-quality image reconstruction from down-sampled or noisy projection data. In this paper, a new prior information obtained from probabilistic atlas is proposed for low-dose CT image reconstruction. Methods: The proposed approach consists of two main phases. In learning phase, a dataset of images obtained from different patients is used to construct a 3D atlas with Laplacian mixture model. The expectation maximization (EM) algorithm is used to estimate the mixture parameters. In reconstruction phase, prior information obtained from the probabilistic atlas is used to construct the cost function for image reconstruction. Results: We investigate the low-dose imaging by considering the reduction of X-ray beam intensity and by acquiring the projection data through a small number of views or limited view angles. Experimental studies using simulated data and chest screening CT data demonstrate that the probabilistic atlas prior is a practically promising approach for the low-dose CT imaging. Conclusions: The prior information obtained from probabilistic atlas constructed from earlier scans of different patients is useful in low-dose CT imaging.