title: An Extended Bayesian-FBP Algorithm.
abstract: Recently we developed a Bayesian-FBP (Filtered Backprojection) algorithm for CT image reconstruction. This algorithm is also referred to as the FBP-MAP (FBP Maximuma Posteriori) algorithm. This non-iterative Bayesian algorithm has been applied to real-time MRI, in which the k-space is under-sampled. This current paper investigates the possibility to extend this Bayesian-FBP algorithm by introducing more controlling parameters. Thus, our original Bayesian-FBP algorithm became a special case of the extended Bayesian-FBP algorithm. A cardiac patient data set is used in this paper to evaluate the extended Bayesian-FBP algorithm, and the result from a well-establish iterative algorithm with L1-norms is used as the gold standard. If the parameters are selected properly, the extended Bayesian-FBP algorithm can outperform the original Bayesian-FBP algorithm.
authors.affiliation: Department of Radiology, University of Utah, Salt Lake City, UT 84108, USA and Department of Engineering, Weber State University, Ogden, UT 84408, USA, (801) 581-3918.
authors.forename: Gengsheng L
authors.initials: GL
authors.lastname: Zeng
authors.affiliation: Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, UT 841112, USA.
authors.forename: Zeljko
authors.initials: Z
authors.lastname: Divkovic
keywords: Analytical reconstruction; Dynamic imaging; Filtered backprojection; MAP objective function; MRI; Real time imaging