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.
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.
Authors: Kevin P Murphy; Lee Crush; Maria Twomey; Patrick D McLaughlin; Iris C Mildenberger; Niamh Moore; Jackie Bye; Owen J O'Connor; Sean E McSweeney; Fergus Shanahan; Michael M Maher Journal: AJR Am J Roentgenol Date: 2015-12 Impact factor: 3.959
Authors: Paul A Yushkevich; Joseph Piven; Heather Cody Hazlett; Rachel Gimpel Smith; Sean Ho; James C Gee; Guido Gerig Journal: Neuroimage Date: 2006-03-20 Impact factor: 6.556
Authors: Mark Oehmigen; Susanne Ziegler; Bjoern W Jakoby; Jens-Christoph Georgi; Daniel H Paulus; Harald H Quick Journal: J Nucl Med Date: 2014-07-08 Impact factor: 10.057
Authors: Maximilian J Waldner; Timo Rath; Sebastian Schürmann; Christian Bojarski; Raja Atreya Journal: Front Immunol Date: 2017-10-11 Impact factor: 7.561