共 55 条
[11]
Akamatsu G(2016)Phantom criteria for qualification of brain FDG and amyloid PET across different cameras EJNMMI Phys 60 R115-359
[12]
Iwao Y(2004)Survey over image thresholding techniques and quantitative performance evaluation J Electron Imaging 56 145-15
[13]
Tashima H(2015)PET-MRI: a review of challenges and solutions in the development of integrated multimodality imaging Phys Med Biol 50 1187-184
[14]
Yoshida E(2023)Quantitative PET/CT scanner performance characterization based upon the Society of Nuclear Medicine and Molecular Imaging Clinical Trials Network oncology clinical simulator phantom J Nucl Med 147 346-1572
[15]
Yamaya T(2023)Qualification of PET scanners for use in multicenter cancer clinical trials: the American College of Radiology Imaging Network experience J Nucl Med 5 1-477
[16]
Chen W(2017)A multi-centre evaluation of eleven clinically feasible brain PET/MRI attenuation correction techniques using a large cohort of patients Neuroimage 5 160-2233
[17]
Marinelli M(2018)A deep learning approach for 18F-FDG PET attenuation correction EJNMMI Phys 33 1563-undefined
[18]
Positano V(2019)Synthetic CT generation from non-attenuation corrected PET images for whole-body PET imaging Phys Med Biol 16 469-undefined
[19]
Tucci F(2021)A review of deep-learning-based approaches for attenuation correction in positron emission tomography IEEE Trans Radiat Plasma Med Sci 31 2224-undefined
[20]
Neglia D(2014)ML-reconstruction for TOF-PET with simultaneous estimation of the attenuation factors IEEE Trans Med Imaging 65 2101-undefined