ArticleEJNMMI research2023
Imaging quality of an artificial intelligence denoising algorithm: validation in
Article in EJNMMI research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Emerging frontiers and challenges of artificial intelligence in PSMA-PET imaging: pioneering a new chapter in prostate cancer care.Japanese journal of radiology · 2026Review
- Article
- Application and prospect of artificial intelligence in diagnostic imaging of prostate cancer.NPJ digital medicine · 2026Review
- Superiority of long axial field-of-viewEuropean journal of nuclear medicine and molecular imaging · 2026Article
- 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
- Review
- AI-Based Noise-Reduction Filter for Whole-Body Planar Bone Scintigraphy Reliably Improves Low-Count Images.Diagnostics (Basel, Switzerland) · 2024Article
- Deep learning-based PET image denoising and reconstruction: a review.Radiological physics and technology · 2024Review
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background
methodsWe retrospectively included 30 patients with biochemical recurrence of prostate cancer who had undergone
resultsVPFX-derived series were classified differently but better than standard reconstructions (p < 0.001) using half the data. Q.Clear series were not classified differently using half the signal. Some series were noisy but had no significant effect on lesion detectability (p > 0.05). The SubtlePET® algorithm significantly decreased lesion SUV (p < 0.005) and increased liver background (p < 0.005) and had no substantial effect on the diagnostic performance of each reader.
conclusionWe show that the SubtlePET® can be used for
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.