Affiliated with RCfalse
Status已發表Published
Low-Dose 68 Ga-PSMA Prostate PET/MRI Imaging Using Deep Learning Based on MRI Priors
Deng, Fuquan1,2,3; Li, Xiaoyuan4; Yang, Fengjiao4; Sun, Hongwei5; Yuan, Jianmin6; He, Qiang6; Xu, Weifeng2; Yang, Yongfeng1,3; Liang, Dong1,3; Liu, Xin1,3; Mok, Greta S.P.7; Zheng, Hairong1,3; Hu, Zhanli1,3
2022-01-26
Source PublicationFrontiers in Oncology
ISSN2234-943X
Volume11
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.

KeywordDeep Learning Discrete Wavelet Transform Low-dose Restoration Pet/mri Prostate
DOI10.3389/fonc.2021.818329
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaOncology
WOS SubjectOncology
WOS IDWOS:000752718100001
Scopus ID2-s2.0-85124510984
Fulltext Access
Citation statistics
Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF ELECTRICAL AND COMPUTER ENGINEERING
Corresponding AuthorHu, Zhanli
Affiliation1.Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
2.Computer Department, North China Electric Power University, Baoding, China
3.Chinese Academy of Sciences Key Laboratory of Health Informatics, Shenzhen, China
4.Department of Nuclear Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
5.United Imaging Research Institute of Intelligent Imaging, Beijing, China
6.Central Research Institute, United Imaging Healthcare Group, Shanghai, China
7.Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Avenida da Universidade, Macao
Recommended Citation
GB/T 7714
Deng, Fuquan,Li, Xiaoyuan,Yang, Fengjiao,et al. Low-Dose 68 Ga-PSMA Prostate PET/MRI Imaging Using Deep Learning Based on MRI Priors[J]. Frontiers in Oncology,2022,11.
APA Deng, Fuquan,Li, Xiaoyuan,Yang, Fengjiao,Sun, Hongwei,Yuan, Jianmin,He, Qiang,Xu, Weifeng,Yang, Yongfeng,Liang, Dong,Liu, Xin,Mok, Greta S.P.,Zheng, Hairong,&Hu, Zhanli.(2022).Low-Dose 68 Ga-PSMA Prostate PET/MRI Imaging Using Deep Learning Based on MRI Priors.Frontiers in Oncology,11.
MLA Deng, Fuquan,et al."Low-Dose 68 Ga-PSMA Prostate PET/MRI Imaging Using Deep Learning Based on MRI Priors".Frontiers in Oncology 11(2022).
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Deng, Fuquan]'s Articles
[Li, Xiaoyuan]'s Articles
[Yang, Fengjiao]'s Articles
Baidu academic
Similar articles in Baidu academic
[Deng, Fuquan]'s Articles
[Li, Xiaoyuan]'s Articles
[Yang, Fengjiao]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Deng, Fuquan]'s Articles
[Li, Xiaoyuan]'s Articles
[Yang, Fengjiao]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.