ArticleEJNMMI physics2021
Deep learning-based image quality improvement of
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 19 papers.
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Who cites it
19 citing papers in PubMed.
- Evaluation of the time‑of‑flight-enhanced deep learning image reconstruction method inPhysical and engineering sciences in medicine · 2026Article
- Deep progressive learning reconstruction for fast and low-dose whole-body PET scans in an integrated PET/MR system.BMC medical imaging · 2026Article
- Optimizing acquisition time and injected dose inBMC medical informatics and decision making · 2026Article
- An AI-Based Solution for Denoising Fast-Acquisition [Journal of imaging informatics in medicine · 2026Article
- Investigation of optimal settings for deviceless respiratory synchronization in PET/CT examinations: effects of different reconstructions on image quality.Radiological physics and technology · 2025Article
- Innovations in clinical PET image reconstruction: advances in Bayesian penalized likelihood algorithm and deep learning.Annals of nuclear medicine · 2025Review
- Deep learning-based time-of-flight (ToF) enhancement of non-ToF PET scans for different radiotracers.European journal of nuclear medicine and molecular imaging · 2025Article
- Article
- Effects of a deep learning-based image quality enhancement method on a digital-BGO PET/CT system forEJNMMI physics · 2025Article
- Deep learning-based PET image denoising and reconstruction: a review.Radiological physics and technology · 2024Review
- Deep convolutional neural network for differentiating between sarcoidosis and lymphoma based on [European radiology · 2024Article
- Acquisition time reduction in pediatricJournal of applied clinical medical physics · 2023Article
- Deep Learning-Based Image Quality Improvement in Digital Positron Emission Tomography for Breast Cancer.Diagnostics (Basel, Switzerland) · 2023Article
- Comparison of the performances of machine learning and deep learning in improving the quality of low dose lung cancer PET images.Japanese journal of radiology · 2022Article
- Enhancement ofDiagnostics (Basel, Switzerland) · 2022Article
- Virtual high-count PET image generation using a deep learning method.Medical physics · 2022Article
- Deep learning-based time-of-flight (ToF) image enhancement of non-ToF PET scans.European journal of nuclear medicine and molecular imaging · 2022Article
- Deep learning-assisted PET imaging achieves fast scan/low-dose examination.EJNMMI physics · 2022Article
- Artificial Intelligence-Based Image Enhancement in PET Imaging: Noise Reduction and Resolution Enhancement.PET clinics · 2021Review
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDeep learning (DL)-based image quality improvement is a novel technique based on convolutional neural networks. The aim of this study was to compare the clinical value of
methodsFifty patients with a mean age of 64.4 (range, 19-88) years who underwent
resultsImages acquired using the DL method scored significantly higher for tumor delineation, overall image quality, and image noise compared to baseline (P < 0.001). The Fleiss' kappa value for overall inter-reader agreement was 0.78. The standardized uptake values in tumor obtained by DL were significantly higher than those acquired using a Gaussian filter (P < 0.001).
conclusionsDeep learning method improves the quality of PET images.
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