An Extended Bayesian-FBP Algorithm
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...
| Publié dans: | IEEE transactions on nuclear science. - 1988. - 63(2016), 1 vom: 13. Feb., Seite 151-156 |
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| Format: | Article |
| Langue: | English |
| Publié: |
2016
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| Accès à la collection: | IEEE transactions on nuclear science |
| Sujets: | Journal Article Analytical reconstruction Dynamic imaging Filtered backprojection MAP objective function MRI Real time imaging |
| Résumé: | 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 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 |
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| Description: | Date Revised 20.11.2019 published: Print-Electronic Citation Status Publisher |
| ISSN: | 0018-9499 |