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Chen KT, Tesfay R, Koran MEI, Ouyang J, Shams S, Young CB, Davidzon G, Liang T, Khalighi M, Mormino E, Zaharchuk G. Generative Adversarial Network-Enhanced Ultra-Low-Dose [ 18F]-PI-2620 τ PET/MRI in Aging and Neurodegenerative Populations. AJNR Am J Neuroradiol 2023; 44:1012-1019. [PMID: 37591771 PMCID: PMC10494955 DOI: 10.3174/ajnr.a7961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 07/11/2023] [Indexed: 08/19/2023]
Abstract
BACKGROUND AND PURPOSE With the utility of hybrid τ PET/MR imaging in the screening, diagnosis, and follow-up of individuals with neurodegenerative diseases, we investigated whether deep learning techniques can be used in enhancing ultra-low-dose [18F]-PI-2620 τ PET/MR images to produce diagnostic-quality images. MATERIALS AND METHODS Forty-four healthy aging participants and patients with neurodegenerative diseases were recruited for this study, and [18F]-PI-2620 τ PET/MR data were simultaneously acquired. A generative adversarial network was trained to enhance ultra-low-dose τ images, which were reconstructed from a random sampling of 1/20 (approximately 5% of original count level) of the original full-dose data. MR images were also used as additional input channels. Region-based analyses as well as a reader study were conducted to assess the image quality of the enhanced images compared with their full-dose counterparts. RESULTS The enhanced ultra-low-dose τ images showed apparent noise reduction compared with the ultra-low-dose images. The regional standard uptake value ratios showed that while, in general, there is an underestimation for both image types, especially in regions with higher uptake, when focusing on the healthy-but-amyloid-positive population (with relatively lower τ uptake), this bias was reduced in the enhanced ultra-low-dose images. The radiotracer uptake patterns in the enhanced images were read accurately compared with their full-dose counterparts. CONCLUSIONS The clinical readings of deep learning-enhanced ultra-low-dose τ PET images were consistent with those performed with full-dose imaging, suggesting the possibility of reducing the dose and enabling more frequent examinations for dementia monitoring.
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Affiliation(s)
- K T Chen
- From the Department of Biomedical Engineering (K.T.C.), National Taiwan University, Taipei, Taiwan
- Department of Radiology (K.T.C., M.E.I.K., J.O., S.S., G.D., T.L., M.K., G.Z.), Stanford University, Stanford, California
| | - R Tesfay
- Meharry Medical College (R.T.), Nashville, Tennessee
| | - M E I Koran
- Department of Radiology (K.T.C., M.E.I.K., J.O., S.S., G.D., T.L., M.K., G.Z.), Stanford University, Stanford, California
| | - J Ouyang
- Department of Radiology (K.T.C., M.E.I.K., J.O., S.S., G.D., T.L., M.K., G.Z.), Stanford University, Stanford, California
| | - S Shams
- Department of Radiology (K.T.C., M.E.I.K., J.O., S.S., G.D., T.L., M.K., G.Z.), Stanford University, Stanford, California
| | - C B Young
- Department of Neurology and Neurological Sciences (C.B.Y., E.M.), Stanford University, Stanford, California
| | - G Davidzon
- Department of Radiology (K.T.C., M.E.I.K., J.O., S.S., G.D., T.L., M.K., G.Z.), Stanford University, Stanford, California
| | - T Liang
- Department of Radiology (K.T.C., M.E.I.K., J.O., S.S., G.D., T.L., M.K., G.Z.), Stanford University, Stanford, California
| | - M Khalighi
- Department of Radiology (K.T.C., M.E.I.K., J.O., S.S., G.D., T.L., M.K., G.Z.), Stanford University, Stanford, California
| | - E Mormino
- Department of Neurology and Neurological Sciences (C.B.Y., E.M.), Stanford University, Stanford, California
| | - G Zaharchuk
- Department of Radiology (K.T.C., M.E.I.K., J.O., S.S., G.D., T.L., M.K., G.Z.), Stanford University, Stanford, California
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Chen K, Adeyeri O, Toueg T, Zeineh M, Mormino E, Khalighi M, Zaharchuk G. Investigating Simultaneity for Deep Learning-Enhanced Actual Ultra-Low-Dose Amyloid PET/MR Imaging. AJNR Am J Neuroradiol 2022; 43:354-360. [PMID: 35086799 PMCID: PMC8910791 DOI: 10.3174/ajnr.a7410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 11/15/2021] [Indexed: 01/27/2023]
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.
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Affiliation(s)
- K.T. Chen
- From the Department of Radiology (K.T.C., M.Z., M.K., G.Z.), Stanford University, Stanford, California,Department of Biomedical Engineering (K.T.C.), National Taiwan University, Taipei, Taiwan
| | - O. Adeyeri
- Department of Computer Science (O.A.), Salem State University, Salem, Massachusetts
| | - T.N. Toueg
- Department of Neurology and Neurological Sciences (T.N.T., E.M.), Stanford University, Stanford, California
| | - M. Zeineh
- From the Department of Radiology (K.T.C., M.Z., M.K., G.Z.), Stanford University, Stanford, California
| | - E. Mormino
- Department of Neurology and Neurological Sciences (T.N.T., E.M.), Stanford University, Stanford, California
| | - M. Khalighi
- From the Department of Radiology (K.T.C., M.Z., M.K., G.Z.), Stanford University, Stanford, California
| | - G. Zaharchuk
- From the Department of Radiology (K.T.C., M.Z., M.K., G.Z.), Stanford University, Stanford, California
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