ReviewPET clinics2021
Artificial Intelligence-Based Image Enhancement in PET Imaging: Noise Reduction and Resolution Enhancement.
Review in PET clinics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 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
37 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The historical progression of positron emission tomography research in neuroendocrinology.Frontiers in neuroendocrinology · 2023Pooled it
- Patlak-Guided Self-Supervised Learning for Dynamic PET Denoising.IEEE transactions on radiation and plasma medical sciences · 2026Article
- Artificial Intelligence for In-Flight Detection of Space-Related Ocular Trauma: Bridging Diagnostic Gaps in Microgravity.Vision (Basel, Switzerland) · 2026Review
- Denoising of 4D dynamic PET images using spatiotemporal regularization with integrated temporal restoration (SPRINTER).EJNMMI physics · 2026Article
- Does Q.Clear Processing Change PET Ratios? Quantitative Evidence Using BTXBrain-DAT.Brain sciences · 2025Article
- Brown adipose tissue machine learning nnU-Net V2 network using TriDFusion (3DF).EJNMMI research · 2025Article
- Comprehensive review of artifacts in cardiac MRI and their mitigation.The international journal of cardiovascular imaging · 2024Review
- Review
- An analytic, moment-based method to estimate orthopositronium lifetimes in positron annihilation lifetime spectroscopy measurements.Bio-algorithms and med-systems · 2024Article
- Assessment of resectability of pancreatic cancer using novel immersive high-performance virtual reality rendering of abdominal computed tomography and magnetic resonance imaging.International journal of computer assisted radiology and surgery · 2024Article
- Emerging paradigms in microwave imaging technology for biomedical applications: unleashing the power of artificial intelligence.Npj imaging · 2024Review
- Application of PET/MRI in Gynecologic Malignancies.Cancers · 2024Review
- Revealing speckle obscured living human retinal cells with artificial intelligence assisted adaptive optics optical coherence tomography.Communications medicine · 2024Article
- Age Encoded Adversarial Learning for Pediatric CT Segmentation.Bioengineering (Basel, Switzerland) · 2024Article
- Deep learning-based PET image denoising and reconstruction: a review.Radiological physics and technology · 2024Review
- Article
- Evaluating deep learning techniques for identifying tongue features in subthreshold depression: a prospective observational study.Frontiers in psychiatry · 2024Article
- Whole-body PET image denoising for reduced acquisition time.Frontiers in medicine · 2024Article
- FedFTN: Personalized federated learning with deep feature transformation network for multi-institutional low-count PET denoising.Medical image analysis · 2023Article
- Deep learning to automate SPECT MPI myocardial reorientation.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 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
6 authors.
Funding
Abstract
High noise and low spatial resolution are two key confounding factors that limit the qualitative and quantitative accuracy of PET images. Artificial intelligence models for image denoising and deblurring are becoming increasingly popular for the postreconstruction enhancement of PET images. We present a detailed review of recent efforts for artificial intelligence-based PET image enhancement with a focus on network architectures, data types, loss functions, and evaluation metrics. We also highlight emerging areas in this field that are quickly gaining popularity, identify barriers to large-scale adoption of artificial intelligence models for PET image enhancement, and discuss future directions.
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.