ReviewRadiological physics and technology2024
Deep learning-based PET image denoising and reconstruction: a review.
Review in Radiological physics and technology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 40 papers.
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
40 citing papers in PubMed.
- Cross-Modality Deep Learning Denoising for Low-Dose μSPECT: Transfer of PET-Trained U‑Net and Diffusion Models.Chemical & biomedical imaging · 2026Article
- Improving dual-panel in-beam PET imaging for proton therapy monitoring using 3D U-Net.Medical physics · 2026Article
- Applications of artificial intelligence in nuclear medicine.Zeitschrift fur medizinische Physik · 2026Review
- Technical Optimization Strategies for Amyloid PET Under Challenging Acquisition Conditions: A Comprehensive Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- An AI-Based Solution for Denoising Fast-Acquisition [Journal of imaging informatics in medicine · 2026Article
- PET Image Reconstruction Using Deep Diffusion Image Prior.IEEE transactions on medical imaging · 2026Article
- Physiological uptake patterns of gastric lymph node stations onQuantitative imaging in medicine and surgery · 2026Article
- SPECT and PET imaging of Alzheimer's disease revisited: from biomarkers to artificial intelligence-based prediction.Annals of nuclear medicine · 2026Review
- Artificial intelligence across the cardiovascular diagnostic pathway: a case-based narrative review.Cardiovascular diagnosis and therapy · 2026Review
- Depth-Dependent Performance of Residual Networks for Low-Count PET Image Restoration Using a Dedicated 3D-Printed Striatum Phantom.Bioengineering (Basel, Switzerland) · 2026Article
- LeqMod: Adaptable Lesion-Quantification-Consistent Modulation for Deep Learning Low-Count PET Image Denoising.IEEE transactions on medical imaging · 2026Article
- Filling of incomplete sinograms from sparse PET detector configurations using a residual U-Net.Medical physics · 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
- Development of a Prototype AWSM-PET Device for Augmented Whole-Body PET Imaging and Initial Human Study.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2026Article
- Impact of Deep-Learning-Based Respiratory Motion Correction on [Biomedicines · 2026Article
- Development of AI-based dopamine transporter (DAT) image generation technique using early phase [18F]-FP-CIT PET imaging.PloS one · 2026Article
- AI-enabled multimodal neuroimaging for neurotransmitter mapping in normal aging and age-related disease.Frontiers in aging neuroscience · 2026Review
- Dual-domain transformer-based learned primal-dual reconstruction for PET imaging.Frontiers in nuclear medicine · 2026Article
- Improved denoising scheme using three-dimensional multi-zone convolutional neural filters in dedicated breast positron emission tomography images.Radiological physics and technology · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
6 authors.
Funding
Abstract
This review focuses on positron emission tomography (PET) imaging algorithms and traces the evolution of PET image reconstruction methods. First, we provide an overview of conventional PET image reconstruction methods from filtered backprojection through to recent iterative PET image reconstruction algorithms, and then review deep learning methods for PET data up to the latest innovations within three main categories. The first category involves post-processing methods for PET image denoising. The second category comprises direct image reconstruction methods that learn mappings from sinograms to the reconstructed images in an end-to-end manner. The third category comprises iterative reconstruction methods that combine conventional iterative image reconstruction with neural-network enhancement. We discuss future perspectives on PET imaging and deep learning technology.
Indexed as
Identifiers
What Socratic 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 Socratic graph.