Abstract #3805

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

Qiyang Zhang^1,2, 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

Synopsis

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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