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Initial Experience With Low-Dose 18F-Fluorodeoxyglucose Positron Emission Tomography/Magnetic Resonance Imaging With Deep Learning Enhancement
- Source :
- J Comput Assist Tomogr
- Publication Year :
- 2021
- Publisher :
- Ovid Technologies (Wolters Kluwer Health), 2021.
-
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 18F-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.
- Subjects :
- Adult
Male
Multimodal Imaging
Article
Fluorodeoxyglucose positron emission tomography
Young Adult
Deep Learning
Fluorodeoxyglucose F18
Image Interpretation, Computer-Assisted
medicine
Humans
Radiology, Nuclear Medicine and imaging
Data input
Aged
medicine.diagnostic_test
Crohn disease
business.industry
Deep learning
Low dose
Magnetic resonance imaging
Middle Aged
Magnetic Resonance Imaging
Mr imaging
Positron emission tomography
Positron-Emission Tomography
Female
Artificial intelligence
Radiopharmaceuticals
Nuclear medicine
business
Subjects
Details
- ISSN :
- 15323145 and 03638715
- Volume :
- 45
- Database :
- OpenAIRE
- Journal :
- Journal of Computer Assisted Tomography
- Accession number :
- edsair.doi.dedup.....61f88eeeadc535387f26d607c2da1328