ReviewEuropean journal of nuclear medicine and molecular imaging2022
Deep learning-based image reconstruction and post-processing methods in positron emission tomography for low-dose imaging and resolution enhancement.
Review in European journal of nuclear medicine and molecular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 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
- Enhancing automated fracture detection in paediatric wrist X-rays with paired and unpaired cast suppression methods.International journal of computer assisted radiology and surgery · 2026Article
- Applications of artificial intelligence in nuclear medicine.Zeitschrift fur medizinische Physik · 2026Review
- Critical review of partial volume correction methods in PET and SPECT imaging: benefits, pitfalls, challenges, and future outlook.European journal of nuclear medicine and molecular imaging · 2026Review
- SynPoC: a high-quality generative diffusion model for transforming ultra-low-field point-of-care MRI using high-field MRI representations.Scientific reports · 2026Article
- Light-RepViTSR: ultra-lightweight super-resolution for real-time photoacoustic endoscopy in tumor biopsy.Frontiers in bioengineering and biotechnology · 2026Article
- AI-enabled multimodal neuroimaging for neurotransmitter mapping in normal aging and age-related disease.Frontiers in aging neuroscience · 2026Review
- 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
- Feasibility study of unsupervised anomaly detection using Wasserstein GAN in SPECT image.EJNMMI physics · 2025Article
- Artificial intelligence for radiopharmaceutical and molecular imaging.Acta pharmaceutica Sinica. B · 2025Review
- Less may be more in PET (with AI).European radiology · 2025Article
- Modern Bioimaging Techniques for Elemental Tissue Analysis: Key Parameters, Challenges and Medical Impact.Molecules (Basel, Switzerland) · 2025Review
- Deep learning in nuclear medicine: from imaging to therapy.Annals of nuclear medicine · 2025Review
- Comparison ofEJNMMI physics · 2025Article
- AI-Driven Advances in Low-Dose Imaging and Enhancement-A Review.Diagnostics (Basel, Switzerland) · 2025Review
- Single-scan adaptive graph filtering for dynamic PET denoising by exploring intrinsic spatio-temporal structure.Frontiers in medicine · 2025Article
- Machine Learning and Deep Learning Applications in Magnetic Particle Imaging.Journal of magnetic resonance imaging : JMRI · 2025Review
- Recent Breakthroughs in PET-CT Multimodality Imaging: Innovations and Clinical Impact.Bioengineering (Basel, Switzerland) · 2024Review
- Deep learning for 3D vascular segmentation in hierarchical phase contrast tomography: a case study on kidney.Scientific reports · 2024Article
- A Comparative Study of AI-Based Automated and Manual Postprocessing of Head and Neck CT Angiography: An Independent External Validation with Multi-Vendor and Multi-Center Data.Neuroradiology · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Image processing plays a crucial role in maximising diagnostic quality of positron emission tomography (PET) images. Recently, deep learning methods developed across many fields have shown tremendous potential when applied to medical image enhancement, resulting in a rich and rapidly advancing literature surrounding this subject. This review encapsulates methods for integrating deep learning into PET image reconstruction and post-processing for low-dose imaging and resolution enhancement. A brief introduction to conventional image processing techniques in PET is firstly presented. We then review methods which integrate deep learning into the image reconstruction framework as either deep learning-based regularisation or as a fully data-driven mapping from measured signal to images. Deep learning-based post-processing methods for low-dose imaging, temporal resolution enhancement and spatial resolution enhancement are also reviewed. Finally, the challenges associated with applying deep learning to enhance PET images in the clinical setting are discussed and future research directions to address these challenges are presented.
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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 Socratic graph.