Initial Experience With Low-Dose 18F-Fluorodeoxyglucose Positron Emission Tomography/Magnetic Resonance Imaging With Deep Learning Enhancement. Issue 4 (July 2021)

• Record Type: Journal Article
• Authors: Park, Christian J.; Chen, Weijie; Pirasteh, Ali; Kim, David H.; Perlman, Scott B.; Robbins, Jessica B.; McMillan, Alan B.
• Is Part Of: Journal of computer assisted tomography. Volume 45:Issue 4(2021)
• Publication Date: 2021-07
• Subjects: positron emission tomography -- magnetic resonance imaging -- deep learning -- artificial intelligence -- radiation exposure; Tomography -- Periodicals; Tomography -- Periodicals; Tomography; Periodicals; 616.0757
• DOI: 10.1097/RCT.0000000000001174
• Languages: English
• ISSNs: 0363-8715

Abstract

Abstract : Objective: To demonstrate the utility of deep learning enhancement (DLE) to achieve diagnostic quality low-dose positron emission tomography (PET)/magnetic resonance (MR) imaging. Methods: Twenty subjects with known Crohn disease underwent simultaneous PET/MR imaging after intravenous administration of approximately 185 MBq of 18 F-fluorodeoxyglucose (FDG). Five image sets were generated: (1) standard-of-care (reference), (2) low-dose (ie, using 20% of PET counts), (3) DLE-enhanced low-dose using PET data as input, (4) DLE-enhanced low-dose using PET and MR data as input, and (5) DLE-enhanced using no PET data input. Image sets were evaluated by both quantitative metrics and qualitatively by expert readers. Results: Although low-dose images (series 2) and images with no PET data input (series 5) were nondiagnostic, DLE of the low-dose images (series 3 and 4) achieved diagnostic quality images that scored more favorably than reference (series 1), both qualitatively and quantitatively. Conclusions: Deep learning enhancement has the potential to enable a 90% reduction of radiotracer while achieving diagnostic quality images.