Bidirectional dynamic frame prediction network for total-body [68Ga]Ga-PSMA-11 and [68Ga]Ga-FAPI-04 PET images

• Published in: EJNMMI physics Vol. 11; no. 1; pp. 92 - 20
• Main Authors: Yang, Qianyi, Li, Wenbo, Huang, Zhenxing, Chen, Zixiang, Zhao, Wenjie, Gao, Yunlong, Yang, Xinlan, Yang, Yongfeng, Zheng, Hairong, Liang, Dong, Liu, Jianjun, Chen, Ruohua, Hu, Zhanli
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 04.11.2024; Springer Nature B.V; SpringerOpen
• Subjects: Applied and Technical Physics; Cancer; Computational Mathematics and Numerical Analysis; Data acquisition; Deep learning; Engineering; Frame prediction; Image acquisition; Image generation; Image quality; Imaging; Medical diagnosis; Medicine; Medicine & Public Health; Metabolism; Noise measurement; Nuclear Medicine; Original Research; Performance evaluation; Pharmacology; Physics; Positron emission; Prostate; Quality assessment; Quantitative analysis; Radiology; Signal to noise ratio; Statistical analysis; Tomography; Total-body dynamic PET; Tumors; Visual observation; Deep learning; Total-body dynamic PET; Frame prediction; Image generation

Purpose Total-body dynamic positron emission tomography (PET) imaging with total-body coverage and ultrahigh sensitivity has played an important role in accurate tracer kinetic analyses in physiology, biochemistry, and pharmacology. However, dynamic PET scans typically entail prolonged durations ( 6...