Low-Dose 68 Ga-PSMA Prostate PET/MRI Imaging Using Deep Learning Based on MRI Priors

Frontiers in Oncology

Oncology

Greta S. P. Mok¹  Jianmin Yuan²  Qiang He²  Weifeng Xu³  Fengjiao Yang⁴  Xiaoyuan Li⁴  Zhanli Hu⁵  Dong Liang⁵  Hairong Zheng⁵  Yongfeng Yang⁵  Xin Liu⁵  Fuquan Deng⁶  Hongwei Sun⁷ 

[1] Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Avenida da Universidade, Macau SAR, China;Central Research Institute, United Imaging Healthcare Group, Shanghai, China;Computer Department, North China Electric Power University, Baoding, China;Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China;Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China;Chinese Academy of Sciences Key Laboratory of Health Informatics, Shenzhen, China;Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China;Computer Department, North China Electric Power University, Baoding, China;Chinese Academy of Sciences Key Laboratory of Health Informatics, Shenzhen, China;United Imaging Research Institute of Intelligent Imaging, Beijing, China;

关键词: PET/MRI;    prostate;    low-dose restoration;    deep learning;    discrete wavelet transform;   

DOI  :  10.3389/fonc.2021.818329

 received in 2021-11-19, accepted in 2021-12-27,  发布年份 2022

来源: Frontiers

【 摘 要 】

Background68 Ga-prostate-specific membrane antigen (PSMA) PET/MRI has become an effective imaging method for prostate cancer. The purpose of this study was to use deep learning methods to perform low-dose image restoration on PSMA PET/MRI and to evaluate the effect of synthesis on the images and the medical diagnosis of patients at risk of prostate cancer.MethodsWe reviewed the 68 Ga-PSMA PET/MRI data of 41 patients. The low-dose PET (LDPET) images of these patients were restored to full-dose PET (FDPET) images through a deep learning method based on MRI priors. The synthesized images were evaluated according to quantitative scores from nuclear medicine doctors and multiple imaging indicators, such as peak-signal noise ratio (PSNR), structural similarity (SSIM), normalization mean square error (NMSE), and relative contrast-to-noise ratio (RCNR).ResultsThe clinical quantitative scores of the FDPET images synthesized from 25%- and 50%-dose images based on MRI priors were 3.84±0.36 and 4.03±0.17, respectively, which were higher than the scores of the target images. Correspondingly, the PSNR, SSIM, NMSE, and RCNR values of the FDPET images synthesized from 50%-dose PET images based on MRI priors were 39.88±3.83, 0.896±0.092, 0.012±0.007, and 0.996±0.080, respectively.ConclusionAccording to a combination of quantitative scores from nuclear medicine doctors and evaluations with multiple image indicators, the synthesis of FDPET images based on MRI priors using and 50%-dose PET images did not affect the clinical diagnosis of prostate cancer. Prostate cancer patients can undergo 68 Ga-PSMA prostate PET/MRI scans with radiation doses reduced by up to 50% through the use of deep learning methods to synthesize FDPET images.

【 授权许可】

Unknown   
Copyright © 2022 Deng, Li, Yang, Sun, Yuan, He, Xu, Yang, Liang, Liu, Mok, Zheng and Hu