title: Probabilistic atlas prior for CT image reconstruction.
abstract: OBJECTIVE
  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.
authors.affiliation: Image Science Lab., Department of Mathematics, Faculty of Science, Suez Canal University, Ismailia 41522, Egypt; Division of Information Engineering, Faculty of Engineering, Information and Systems, University of Tsukuba, Tennoudai1-1-1, Tsukuba 305-8573, Japan. Electronic address: erashed@science.suez.edu.eg.
authors.forename: Essam A
authors.initials: EA
authors.lastname: Rashed
authors.affiliation: Division of Information Engineering, Faculty of Engineering, Information and Systems, University of Tsukuba, Tennoudai1-1-1, Tsukuba 305-8573, Japan; JST-ERATO, Momose Quantum-Beam Phase Imaging Project, Katahira 2-1-1, Aoba-ku, Sendai 980-8577, Japan.
authors.forename: Hiroyuki
authors.initials: H
authors.lastname: Kudo
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D006801:Humans; D007091:Image Processing, Computer-Assisted; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D016012:Poisson Distribution; D011336:Probability; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D014057:Tomography, X-Ray Computed; D014965:X-Rays
keywords: Computed tomography; Laplacian mixture model; Probabilistic atlas; Statistical image reconstruction