K T Chen1,2, O Adeyeri3, T N Toueg4, M Zeineh5, E Mormino4, M Khalighi5, G Zaharchuk5. 1. From the Department of Radiology (K.T.C., M.Z., M.K., G.Z.), Stanford University, Stanford, California chenkt@ntu.edu.tw. 2. Department of Biomedical Engineering (K.T.C.), National Taiwan University, Taipei, Taiwan. 3. Department of Computer Science (O.A.), Salem State University, Salem, Massachusetts. 4. Department of Neurology and Neurological Sciences (T.N.T., E.M.), Stanford University, Stanford, California. 5. From the Department of Radiology (K.T.C., M.Z., M.K., G.Z.), Stanford University, Stanford, California.
Abstract
BACKGROUND AND PURPOSE: Diagnostic-quality amyloid PET images can be created with deep learning using actual ultra-low-dose PET images and simultaneous structural MR imaging. Here, we investigated whether simultaneity is required; if not, MR imaging-assisted ultra-low-dose PET imaging could be performed with separate PET/CT and MR imaging acquisitions. MATERIALS AND METHODS: We recruited 48 participants: Thirty-two (20 women; mean, 67.7 [SD, 7.9] years) were used for pretraining; 328 (SD, 32) MBq of [18F] florbetaben was injected. Sixteen participants (6 women; mean, 71.4 [SD. 8.7] years of age) were scanned in 2 sessions, with 6.5 (SD, 3.8) and 300 (SD, 14) MBq of [18F] florbetaben injected, respectively. Structural MR imaging was acquired simultaneously with PET (90-110 minutes postinjection) on integrated PET/MR imaging in 2 sessions. Multiple U-Net-based deep networks were trained to create diagnostic PET images. For each method, training was done with the ultra-low-dose PET as input combined with MR imaging from either the ultra-low-dose session (simultaneous) or from the standard-dose PET session (nonsimultaneous). Image quality of the enhanced and ultra-low-dose PET images was evaluated using quantitative signal-processing methods, standardized uptake value ratio correlation, and clinical reads. RESULTS: Qualitatively, the enhanced images resembled the standard-dose image for both simultaneous and nonsimultaneous conditions. Three quantitative metrics showed significant improvement for all networks and no differences due to simultaneity. Standardized uptake value ratio correlation was high across different image types and network training methods, and 31/32 enhanced image pairs were read similarly. CONCLUSIONS: This work suggests that accurate amyloid PET images can be generated using enhanced ultra-low-dose PET and either nonsimultaneous or simultaneous MR imaging, broadening the utility of ultra-low-dose amyloid PET imaging.
BACKGROUND AND PURPOSE: Diagnostic-quality amyloid PET images can be created with deep learning using actual ultra-low-dose PET images and simultaneous structural MR imaging. Here, we investigated whether simultaneity is required; if not, MR imaging-assisted ultra-low-dose PET imaging could be performed with separate PET/CT and MR imaging acquisitions. MATERIALS AND METHODS: We recruited 48 participants: Thirty-two (20 women; mean, 67.7 [SD, 7.9] years) were used for pretraining; 328 (SD, 32) MBq of [18F] florbetaben was injected. Sixteen participants (6 women; mean, 71.4 [SD. 8.7] years of age) were scanned in 2 sessions, with 6.5 (SD, 3.8) and 300 (SD, 14) MBq of [18F] florbetaben injected, respectively. Structural MR imaging was acquired simultaneously with PET (90-110 minutes postinjection) on integrated PET/MR imaging in 2 sessions. Multiple U-Net-based deep networks were trained to create diagnostic PET images. For each method, training was done with the ultra-low-dose PET as input combined with MR imaging from either the ultra-low-dose session (simultaneous) or from the standard-dose PET session (nonsimultaneous). Image quality of the enhanced and ultra-low-dose PET images was evaluated using quantitative signal-processing methods, standardized uptake value ratio correlation, and clinical reads. RESULTS: Qualitatively, the enhanced images resembled the standard-dose image for both simultaneous and nonsimultaneous conditions. Three quantitative metrics showed significant improvement for all networks and no differences due to simultaneity. Standardized uptake value ratio correlation was high across different image types and network training methods, and 31/32 enhanced image pairs were read similarly. CONCLUSIONS: This work suggests that accurate amyloid PET images can be generated using enhanced ultra-low-dose PET and either nonsimultaneous or simultaneous MR imaging, broadening the utility of ultra-low-dose amyloid PET imaging.
Authors: Rahul S Desikan; Florent Ségonne; Bruce Fischl; Brian T Quinn; Bradford C Dickerson; Deborah Blacker; Randy L Buckner; Anders M Dale; R Paul Maguire; Bradley T Hyman; Marilyn S Albert; Ronald J Killiany Journal: Neuroimage Date: 2006-03-10 Impact factor: 6.556
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Authors: Kevin T Chen; Enhao Gong; Fabiola Bezerra de Carvalho Macruz; Junshen Xu; Athanasia Boumis; Mehdi Khalighi; Kathleen L Poston; Sharon J Sha; Michael D Greicius; Elizabeth Mormino; John M Pauly; Shyam Srinivas; Greg Zaharchuk Journal: Radiology Date: 2018-12-11 Impact factor: 29.146
Authors: Claes N Ladefoged; Ian Law; Udunna Anazodo; Keith St Lawrence; David Izquierdo-Garcia; Ciprian Catana; Ninon Burgos; M Jorge Cardoso; Sebastien Ourselin; Brian Hutton; Inés Mérida; Nicolas Costes; Alexander Hammers; Didier Benoit; Søren Holm; Meher Juttukonda; Hongyu An; Jorge Cabello; Mathias Lukas; Stephan Nekolla; Sibylle Ziegler; Matthias Fenchel; Bjoern Jakoby; Michael E Casey; Tammie Benzinger; Liselotte Højgaard; Adam E Hansen; Flemming L Andersen Journal: Neuroimage Date: 2016-12-14 Impact factor: 6.556