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

Abstract

BACKGROUND:

68 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.

METHODS:

We 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).

RESULTS:

The 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.

CONCLUSION:

According 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.

• Authors: Fuquan Deng, Xiaoyuan Li, Fengjiao Yang, Hongwei Sun, Jianmin Yuan, Qiang He, Weifeng Xu, Yongfeng Yang, Dong Liang, Xin Liu, Greta S P Mok, Hairong Zheng, Zhanli Hu
• Journal: Frontiers in oncology (Front Oncol) Vol. 11 Pg. 818329 ( 2021) ISSN: 2234-943X [Print] Switzerland
• PMID: 35155207 (Publication Type: Journal Article)
• Copyright: Copyright © 2022 Deng, Li, Yang, Sun, Yuan, He, Xu, Yang, Liang, Liu, Mok, Zheng and Hu.