[PET] 5. Recent Advances in PET Image Reconstruction Using a Bayesian Penalized Likelihood Algorithm
Author:
Affiliation:
1. Department of Radiological Sciences, School of Health Sciences, Fukushima Medical University
2. Research Team for Neuroimaging, Tokyo Metropolitan Institute of Gerontology
3. Department of Radiology, Chiba University Hospital
Publisher
Japanese Society of Radiological Technology
Subject
General Medicine
Link
https://www.jstage.jst.go.jp/article/jjrt/79/5/79_2023-2200/_pdf
Reference66 articles.
1. 1) Ahn S, Ross SG, Asma E, et al. Quantitative comparison of OSEM and penalized likelihood image reconstruction using relative difference penalties for clinical PET. Phys Med Biol 2015; 60(15): 5733–5751.
2. 2) Nuyts J, Beque D, Dupont P, et al. A concave prior penalizing relative differences for maximum-a-posteriori reconstruction in emission tomography. IEEE Trans Nucl Sci 2002; 49(1): 56–60.
3. 3) Qi J, Leahy RM. Iterative reconstruction techniques in emission computed tomography. Phys Med Biol 2006; 51(15): R541–R578.
4. 4) Nuyts J, Baete K, Beque D, et al. Comparison between MAP and postprocessed ML for image reconstruction in emission tomography when anatomical knowledge is available. IEEE Trans Med Imaging 2005; 24(5): 667–675.
5. 5) Miwa K, Yoshii T, Wagatsuma K, et al. Impact of gamma factor in the penalty function of Bayesian penalized likelihood reconstruction (Q.Clear) to achieve high-resolution PET images. EJNMMI Phys 2023; 10(1): 4.
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