ArticleEJNMMI physics2022
Clinical and phantom validation of a deep learning based denoising algorithm for F-18-FDG PET images from lower detection counting in comparison with the standard acquisition.
Article in EJNMMI physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed, 29 citations in OpenAlex.
- An AI-Based Solution for Denoising Fast-Acquisition [Journal of imaging informatics in medicine · 2026Article
- A rapid total-body PET imaging approach for pediatric patients using non-attenuation-corrected PET scans.EJNMMI physics · 2026Article
- Low-count whole-body PET denoising with deep learning in a multicenter, multi-tracer and externally validated study.European journal of nuclear medicine and molecular imaging · 2026Article
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- Innovations in clinical PET image reconstruction: advances in Bayesian penalized likelihood algorithm and deep learning.Annals of nuclear medicine · 2025Review
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- Dose Reduction in Scintigraphic Imaging Through Enhanced Convolutional Autoencoder-Based Denoising.Journal of imaging · 2025Article
- External phantom-based validation of a deep-learning network trained for upscaling of digital low count PET data.EJNMMI physics · 2025Article
- Similarity and quality metrics for MR image-to-image translation.Scientific reports · 2025Article
- The impact of introducing deep learning based [EJNMMI research · 2024Article
- Deep learning generation of preclinical positron emission tomography (PET) images from low-count PET with task-based performance assessment.Medical physics · 2024Article
- Whole-body PET image denoising for reduced acquisition time.Frontiers in medicine · 2024Article
- Observer studies of image quality of denoising reduced-count cardiac single photon emission computed tomography myocardial perfusion imaging by three-dimensional Gaussian post-reconstruction filtering and deep learning.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023Article
- Feasibility of a deep learning algorithm to achieve the low-doseThe British journal of radiology · 2023Article
- Imaging quality of an artificial intelligence denoising algorithm: validation inEJNMMI research · 2023Article
- Deep Learning Denoising Improves and Homogenizes Patient [Diagnostics (Basel, Switzerland) · 2023Article
- Artificial intelligence-basedFrontiers in medicine · 2023Article
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8 authors at 4 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundPET/CT image quality is directly influenced by the F-18-FDG injected activity. The higher the injected activity, the less noise in the reconstructed images but the more radioactive staff exposition. A new FDA cleared software has been introduced to obtain clinical PET images, acquired at 25% of the count statistics considering US practices. Our aim is to determine the limits of a deep learning based denoising algorithm (SubtlePET) applied to statistically reduced PET raw data from 3 different last generation PET scanners in comparison to the regular acquisition in phantom and patients, considering the European guidelines for radiotracer injection activities. Images of low and high contrasted (SBR = 2 and 5) spheres of the IEC phantom and high contrast (SBR = 5) of micro-spheres of Jaszczak phantom were acquired on 3 different PET devices. 110 patients with different pathologies were included. The data was acquired in list-mode and retrospectively reconstructed with the regular acquisition count statistic (PET100), 50% reduction in counts (PET50) and 66% reduction in counts (PET33). These count reduced images were post-processed with SubtlePET to obtain PET50 + SP and PET33 + SP images. Patient image quality was scored by 2 senior nuclear physicians. Peak-signal-to-Noise and Structural similarity metrics were computed to compare the low count images to regular acquisition (PET100).
resultsSubtlePET reliably denoised the images and maintained the SUV
conclusionBased on our results, SubtlePET is adapted in clinical practice for half-time or half-dose acquisitions based on European recommended injected dose of 3 MBq/kg without diagnostic confidence loss.
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