ArticleEJNMMI physics2021
Artificial intelligence for reduced dose 18F-FDG PET examinations: a real-world deployment through a standardized framework and business case assessment.
Article in EJNMMI physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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43 citing papers in PubMed, 62 citations in OpenAlex.
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- Clinical validation of a deep learning model for low-count PET image enhancement.European journal of nuclear medicine and molecular imaging · 2025Article
- Lung lesion detectability on images obtained from decimated and CNN-based denoised [European journal of nuclear medicine and molecular imaging · 2025Article
- Innovations in clinical PET image reconstruction: advances in Bayesian penalized likelihood algorithm and deep learning.Annals of nuclear medicine · 2025Review
- Implantation of an Artificial Intelligence Denoising Algorithm Using SubtlePET™ with Various Radiotracers: 18F-FDG, 68Ga PSMA-11 and 18F-FDOPA, Impact on the Technologist Radiation Doses.Journal of imaging · 2025Article
- Deep learning image enhancement algorithms in PET/CT imaging: a phantom and sarcoma patient radiomic evaluation.European journal of nuclear medicine and molecular imaging · 2025Article
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- Dose Reduction in Scintigraphic Imaging Through Enhanced Convolutional Autoencoder-Based Denoising.Journal of imaging · 2025Article
- Effects of a deep learning-based image quality enhancement method on a digital-BGO PET/CT system forEJNMMI physics · 2025Article
- Cutting through the hype: the true economic impact and ROI of AI in radiology.European radiology · 2024Article
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Corrections and comments
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Authors and funding
10 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo determine whether artificial intelligence (AI) processed PET/CT images of reduced by one-third of 18-F-FDG activity compared to the standard injected dose, were non-inferior to native scans and if so to assess the potential impact of commercialization. MATERIALS AND
methodsSubtlePET™ AI was introduced in a PET/CT center in Italy. Eligible patients referred for 18F-FDG PET/CT were prospectively enrolled. Administered 18F-FDG was reduced to two-thirds of standard dose. Patients underwent one low-dose CT and two sequential PET scans; "PET-processed" with reduced dose and standard acquisition time, and "PET-native" with an elapsed time to simulate standard acquisition time and dose. PET-processed images were reconstructed using SubtlePET™. PET-native images were defined as the standard of reference. The datasets were anonymized and independently evaluated in random order by four blinded readers. The evaluation included subjective image quality (IQ) assessment, lesion detectability, and assessment of business benefits.
resultsFrom February to April 2020, 61 patients were prospectively enrolled. Subjective IQ was not significantly different between datasets (4.62±0.23, p=0.237) for all scanner models, with "almost perfect" inter-reader agreement. There was no significant difference between datasets in lesions' detectability, target lesion mean SUV
conclusionThis is the first real-world study to demonstrate the non-inferiority of AI processed 18F-FDG PET/CT examinations obtained with 66% standard dose and a methodology to define the AI solution price.
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