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Abstract #3805

Improving image quality of ultralow-dose pediatric total-body PET/CT using deep learning technique

Qiyang Zhang1,2, Zizheng Xiao3, Xu Zhang3, Yingying Hu3, Yumo Zhao3, Jingyi Wang4, Jiatai Feng4, Yun Zhou4, Yongfeng Yang1, Xin Liu1, Hairong Zheng1, Wei Fan3, Dong Liang1, and Zhanli Hu1
1Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China, 2National Innovation Center for High Performance Medical Devices, Shenzhen, China, 3Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China, 4Central Research Institute, United Imaging Healthcare Group, Shanghai, China


Young children are more sensitive to radiation doses than adults, and their absorption of effective doses can be 4-5 times that of adults. When performing PET imaging, the use of low-dose imaging agents for high-quality imaging is of clinical importance. Here, we use artificial intelligence techniques combined with prior CT information to improve the quality of total-body PET/CT images in ultralow-dose pediatric FDG scans, and the results show that the equivalent quality of 600s acquisition data can be obtained using 15s acquisition.

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