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

Abstract Purpose: 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 images of these patients were restored to full-dose PET images through a deep learning method based on MR 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 scores of the full images synthesized from 25%- and 50%-dose images based on MR 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 full-dose images synthesized from 25%-dose PET images based on MR priors were 37.86±4.16, 0.916±0.063, 0.015±0.012, and 1.004±0.126, respectively.Conclusion: According to a combination of quantitative scores from nuclear medicine doctors and evaluations with multiple image indicators, the synthesis of full-dose images based on MR priors using 25%- 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 75% through the use of deep learning methods to synthesize full-dose images.