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 Maximum a 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 L 1 -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.

Application of edge detection based on hexagonal image structure to delamination detection of carbon fiber reinforced polymer material

“…The time-of-flight data obtained from the FE simulation were extracted and FBP algorithm [34] based on fan-beam technology and were applied to image reconstruction to obtain four delamination defect diagrams of composite materials (see figure 10). The proposed method for the square pixel to hexagonal pixel transformation can be evaluated from the interpolation performance and the precision of the edge detection.…”