PET Images Enhancement Using Deep Training of Reconstructed Images with Bayesian Penalized Likelihood Algorithm
Author:
Funder
Tehran University of Medical Sciences and Health Services
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s40846-024-00882-8.pdf
Reference20 articles.
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2. Boellaard, R. (2009). Standards for PET image acquisition and quantitative data analysis. Journal of Nuclear Medicine, 50(Suppl 1), 11s–20s.
3. Naghavi-Behzad, M. (2023). Comparison of image quality and quantification parameters between Q.Clear and OSEM Reconstruction Methods on FDG-PET/CT images in patients with metastatic breast Cancer. J Imaging, 9(3).
4. Otani, T., et al. (2019). Evaluation and optimization of a New PET Reconstruction Algorithm, bayesian penalized Likelihood Reconstruction, for Lung Cancer Assessment according to lesion size. Ajr. American Journal of Roentgenology, 213(2), W50–w56.
5. Hudson, H. M., & Larkin, R. S. (1994). Accelerated image reconstruction using ordered subsets of projection data. Ieee Transactions on Medical Imaging, 13(4), 601–609.
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