ReviewThe British journal of radiology2023
AI for PET image reconstruction.
Review in The British journal of radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic performance of PET-based artificial intelligence for differentiating Parkinson's disease from normal controls or atypical parkinsonism: a systematic review and meta-analysis.Journal of neurology · 2025Pooled it
- Artificial Intelligence Across the Cancer Theranostics Workflow: Critical Appraisal of Current Evidence and Future Clinical Translation.Molecular imaging and biology · 2026Review
- Two-stage deep learning framework for the restoration of incomplete-ring PET images.Medical & biological engineering & computing · 2026Article
- Applications of artificial intelligence in nuclear medicine.Zeitschrift fur medizinische Physik · 2026Review
- Differential performance of statistical versus machine learning methods in partial volume correction in oncologic F18-FDG PET/CT scanning.Annals of nuclear medicine · 2026Article
- Whole-Body Dynamic Positron Emission and Computed Tomography (WBD-PET/CT): Latest Developments, Challenges and Opportunities.Diagnostics (Basel, Switzerland) · 2026Review
- Quantitative Preclinical Imaging as a Metrological Framework: Reproducibility, Validation, and Translational Maturity.Journal of imaging · 2026Review
- A rapid total-body PET imaging approach for pediatric patients using non-attenuation-corrected PET scans.EJNMMI physics · 2026Article
- SPECT and PET imaging of Alzheimer's disease revisited: from biomarkers to artificial intelligence-based prediction.Annals of nuclear medicine · 2026Review
- Variance reduction with synaptic density imaging in Parkinson's disease using direct-4D PET image reconstruction.Physics in medicine and biology · 2026Article
- Image reconstruction and elongation artifact reduction for a dual-panel dedicated prostate PET scanner.Medical physics · 2026Article
- Dual-domain transformer-based learned primal-dual reconstruction for PET imaging.Frontiers in nuclear medicine · 2026Article
- Development of AI-based dopamine transporter (DAT) image generation technique using early phase [18F]-FP-CIT PET imaging.PloS one · 2026Article
- Improved quantification in end-to-end deep learning FastPET reconstruction using multi-view histo-images of attenuation correction factors.IEEE transactions on radiation and plasma medical sciences · 2026Article
- The Effect of AI on the Radiologist Workforce: A Task-Based Analysis.medRxiv : the preprint server for health sciences · 2025Article
- Artificial intelligence in medicine: Current applications in cardiology, oncology, and radiology.World journal of methodology · 2025Review
- Likelihood-Scheduled Score-Based Generative Modeling for Fully 3D PET Image Reconstruction.IEEE transactions on medical imaging · 2025Article
- AI-Driven Advances in Low-Dose Imaging and Enhancement-A Review.Diagnostics (Basel, Switzerland) · 2025Review
- ASICs in PET: what we have and what we need.EJNMMI physics · 2025Review
- A review of state-of-the-art resolution improvement techniques in SPECT imaging.EJNMMI physics · 2025Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Image reconstruction for positron emission tomography (PET) has been developed over many decades, with advances coming from improved modelling of the data statistics and improved modelling of the imaging physics. However, high noise and limited spatial resolution have remained issues in PET imaging, and state-of-the-art PET reconstruction has started to exploit other medical imaging modalities (such as MRI) to assist in noise reduction and enhancement of PET's spatial resolution. Nonetheless, there is an ongoing drive towards not only improving image quality, but also reducing the injected radiation dose and reducing scanning times. While the arrival of new PET scanners (such as total body PET) is helping, there is always a need to improve reconstructed image quality due to the time and count limited imaging conditions. Artificial intelligence (AI) methods are now at the frontier of research for PET image reconstruction. While AI can learn the imaging physics as well as the noise in the data (when given sufficient examples), one of the most common uses of AI arises from exploiting databases of high-quality reference examples, to provide advanced noise compensation and resolution recovery. There are three main AI reconstruction approaches: (i) direct data-driven AI methods which rely on supervised learning from reference data, (ii) iterative (unrolled) methods which combine our physics and statistical models with AI learning from data, and (iii) methods which exploit AI with our known models, but crucially can offer benefits even in the absence of any example training data whatsoever. This article reviews these methods, considering opportunities and challenges of AI for PET reconstruction.
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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.