ArticleEuropean journal of nuclear medicine and molecular imaging2022
Artificial intelligence-based PET denoising could allow a two-fold reduction in [
Article in European journal of nuclear medicine and molecular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed, 53 citations in OpenAlex.
- Review
- 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
- Depth-Dependent Performance of Residual Networks for Low-Count PET Image Restoration Using a Dedicated 3D-Printed Striatum Phantom.Bioengineering (Basel, Switzerland) · 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
- Article
- Scanner-integrated reconstruction versus post-processing deep learning for low-countEJNMMI physics · 2026Article
- Dual-branch residual encoder-decoder convolutional neural network (DB-REDCNN): a computed tomography-integrated multimodal network for positron emission tomography denoising.Quantitative imaging in medicine and surgery · 2025Article
- Clinical validation of a deep learning model for low-count PET image enhancement.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
- Less may be more in PET (with AI).European radiology · 2025Article
- 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 model for low-dose CT late iodine enhancement imaging and extracellular volume quantification.European radiology · 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
- Deep Learning Approaches for Automated Prediction of Treatment Response in Non-Small-Cell Lung Cancer Patients Based on CT and PET Imaging.Tomography (Ann Arbor, Mich.) · 2025Review
- Dose Reduction in Scintigraphic Imaging Through Enhanced Convolutional Autoencoder-Based Denoising.Journal of imaging · 2025Article
- Systematic double reading for oncological PET/CT scans: insights from a prospective multicentre study in 678 patients.EJNMMI reports · 2025Article
- Recent Breakthroughs in PET-CT Multimodality Imaging: Innovations and Clinical Impact.Bioengineering (Basel, Switzerland) · 2024Review
- AI-Based Noise-Reduction Filter for Whole-Body Planar Bone Scintigraphy Reliably Improves Low-Count Images.Diagnostics (Basel, Switzerland) · 2024Article
- Clinical evaluation of deep learning-enhanced lymphoma pet imaging with accelerated acquisition.Journal of applied clinical medical physics · 2024Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeWe investigated whether artificial intelligence (AI)-based denoising halves PET acquisition time in digital PET/CT.
methodsOne hundred ninety-five patients referred for [
resultsPET45 showed mediocre IQ (scored poor in 8% and moderate in 68%) and lesion concordance rate with PET90 (88.7%). In PET45AI, IQ scores were similar to PET90 (P = 0.80), good in 92% and moderate in 8% for both. The lesion concordance rate between PET90 and PET45AI was 836/856 (97.7%), with 7 lesions (0.8%) only detected in PET90 and 13 (1.5%) exclusively in PET45AI. Lesion EARL
conclusionAI allows [
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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.