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- Title
Low-count whole-body PET/MRI restoration: an evaluation of dose reduction spectrum and five state-of-the-art artificial intelligence models.
- Authors
Wang, Yan-Ran; Wang, Pengcheng; Adams, Lisa Christine; Sheybani, Natasha Diba; Qu, Liangqiong; Sarrami, Amir Hossein; Theruvath, Ashok Joseph; Gatidis, Sergios; Ho, Tina; Zhou, Quan; Pribnow, Allison; Thakor, Avnesh S.; Rubin, Daniel; Daldrup-Link, Heike E.
- Abstract
Purpose: To provide a holistic and complete comparison of the five most advanced AI models in the augmentation of low-dose 18F-FDG PET data over the entire dose reduction spectrum. Methods: In this multicenter study, five AI models were investigated for restoring low-count whole-body PET/MRI, covering convolutional benchmarks — U-Net, enhanced deep super-resolution network (EDSR), generative adversarial network (GAN) — and the most cutting-edge image reconstruction transformer models in computer vision to date — Swin transformer image restoration network (SwinIR) and EDSR-ViT (vision transformer). The models were evaluated against six groups of count levels representing the simulated 75%, 50%, 25%, 12.5%, 6.25%, and 1% (extremely ultra-low-count) of the clinical standard 3 MBq/kg 18F-FDG dose. The comparisons were performed upon two independent cohorts — (1) a primary cohort from Stanford University and (2) a cross-continental external validation cohort from Tübingen University — in order to ensure the findings are generalizable. A total of 476 original count and simulated low-count whole-body PET/MRI scans were incorporated into this analysis. Results: For low-count PET restoration on the primary cohort, the mean structural similarity index (SSIM) scores for dose 6.25% were 0.898 (95% CI, 0.887–0.910) for EDSR, 0.893 (0.881–0.905) for EDSR-ViT, 0.873 (0.859–0.887) for GAN, 0.885 (0.873–0.898) for U-Net, and 0.910 (0.900–0.920) for SwinIR. In continuation, SwinIR and U-Net's performances were also discreetly evaluated at each simulated radiotracer dose levels. Using the primary Stanford cohort, the mean diagnostic image quality (DIQ; 5-point Likert scale) scores of SwinIR restoration were 5 (SD, 0) for dose 75%, 4.50 (0.535) for dose 50%, 3.75 (0.463) for dose 25%, 3.25 (0.463) for dose 12.5%, 4 (0.926) for dose 6.25%, and 2.5 (0.534) for dose 1%. Conclusion: Compared to low-count PET images, with near-to or nondiagnostic images at higher dose reduction levels (up to 6.25%), both SwinIR and U-Net significantly improve the diagnostic quality of PET images. A radiotracer dose reduction to 1% of the current clinical standard radiotracer dose is out of scope for current AI techniques.
- Subjects
POSITRON emission tomography computed tomography; RADIOLOGY; CONVOLUTIONAL neural networks; COMPUTER vision; COMPUTER simulation
- Publication
European Journal of Nuclear Medicine & Molecular Imaging, 2023, Vol 50, Issue 5, p1337
- ISSN
1619-7070
- Publication type
Article
- DOI
10.1007/s00259-022-06097-w