ArticleEJNMMI physics2022
Deep learning-assisted PET imaging achieves fast scan/low-dose examination.
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 26 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning-based techniques for estimating high-quality full-dose positron emission tomography images from low-dose scans: a systematic review.BMC medical imaging · 2024Pooled it
- 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
- A Pilot Study on Quantitative Accuracy and Radiomic Feature Stability of Deep Progressive Learning Reconstruction in NSCLC PET.Journal of imaging informatics in medicine · 2026Article
- An AI-Based Solution for Denoising Fast-Acquisition [Journal of imaging informatics in medicine · 2026Article
- Acquisition time/dose reduction in pediatric PET imaging using patch-based deep learning.EJNMMI physics · 2026Article
- Scanner-integrated reconstruction versus post-processing deep learning for low-countEJNMMI physics · 2026Article
- Investigate the quantification accuracy of small lesions in oncologicalBMC medical imaging · 2026Article
- Innovations in clinical PET image reconstruction: advances in Bayesian penalized likelihood algorithm and deep learning.Annals of nuclear medicine · 2025Review
- Article
- EnhancingCancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Impact of a deep progressive reconstruction algorithm on low-dose or fast-scan PET image quality and Deauville score in patients with lymphoma.EJNMMI physics · 2025Article
- Effects of a deep learning-based image quality enhancement method on a digital-BGO PET/CT system forEJNMMI physics · 2025Article
- Deep learning-based low count whole-body positron emission tomography denoising incorporating computed tomography priors.Quantitative imaging in medicine and surgery · 2024Article
- Clinical evaluation of deep learning-enhanced lymphoma pet imaging with accelerated acquisition.Journal of applied clinical medical physics · 2024Article
- Stability of radiomic features from positron emission tomography images: a phantom study comparing advanced reconstruction algorithms and ordered subset expectation maximization.Physical and engineering sciences in medicine · 2024Article
- Fully automated computational measurement of noise in positron emission tomography.European radiology · 2024Article
- LowQuantitative imaging in medicine and surgery · 2024Article
- Walk-through flat panel total-body PET: a patient-centered design for high throughput imaging at lower cost using DOI-capable high-resolution monolithic detectors.European journal of nuclear medicine and molecular imaging · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
purposeThis study aimed to investigate the impact of a deep learning (DL)-based denoising method on the image quality and lesion detectability of
methodsFifty-two oncological patients undergoing an
resultsThe DL method showed superior performance regarding image noise and inferior performance regarding lesion contrast in the qualitative assessment. More than 96.8% of the lesions were successfully identified in DL images. Excellent agreements on SUV in livers and lesions were found. The DL method significantly improved the liver SNR for count reduction down to 33.3% (p < 0.001). Lesion TBR was not significantly different between DL and reference images of the 75% dataset; furthermore, there was no significant difference either for lesions of > 10 mm or lesions in BMIs of > 25. For the 50% dataset, there was no significant difference between DL and reference images for TBR of lesion with > 15 mm or higher uptake than liver.
conclusionsThe developed DL method improved both liver SNR and lesion TBR indicating better image quality and lesion conspicuousness compared to GS method. Compared with the reference, it showed non-inferior image quality with reduced counts by 25-50% under various conditions.
Indexed as
Identifiers
What OpenQuestion holds
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.