Parameter-Transferred Wasserstein Generative Adversarial Network (PT-WGAN) for Low-Dose PET Image Denoising

• Published in: IEEE transactions on radiation and plasma medical sciences Vol. 5; no. 2; pp. 213 - 223
• Main Authors: Gong, Yu, Shan, Hongming, Teng, Yueyang, Tu, Ning, Li, Ming, Liang, Guodong, Wang, Ge, Wang, Shanshan
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Biomedical imaging; Convolution; Deconvolution; Deep learning; Generative adversarial networks; image quality; low-dose positron emission tomography (PET); Medical imaging; Noise reduction; Parameters; Positron emission; Positron emission tomography; Radiation; Radiation dosage; task-specific initialization; Three-dimensional displays; Tomography; Transfer learning; Two dimensional displays; Deep learning; image quality; transfer learning; task-specific initialization; low-dose PET

Due to the widespread of positron emission tomography (PET) in clinical practice, the potential risk of PET-associated radiation dose to patients needs to be minimized. However, with the reduction in the radiation dose, the resultant images may suffer from noise and artifacts that compromise diagnos...