ArticleEuropean journal of nuclear medicine and molecular imaging2025
Impact of deep learning denoising on kinetic modelling for low-dose dynamic PET: application to single- and dual-tracer imaging protocols.
Article in European journal of nuclear medicine and molecular imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Realization of long axial field-of-view PET technology in the clinic and research environment.The British journal of radiology · 2026Review
- Kinetic modeling with total body PET-current status and future applications.The British journal of radiology · 2026Review
- Parametric imaging of dynamic long-axial-field-of-view PET scans: Technical challenges, statistical insights and clinical applications.Zeitschrift fur medizinische Physik · 2026Review
- Applications of artificial intelligence in nuclear medicine.Zeitschrift fur medizinische Physik · 2026Review
- Whole-Body Dynamic Positron Emission and Computed Tomography (WBD-PET/CT): Latest Developments, Challenges and Opportunities.Diagnostics (Basel, Switzerland) · 2026Review
- Exploring extended [European journal of nuclear medicine and molecular imaging · 2026Article
- Variance reduction with synaptic density imaging in Parkinson's disease using direct-4D PET image reconstruction.Physics in medicine and biology · 2026Article
- Eagle eyes on the human brain and beyond: PET at ultra-high spatial resolution.European journal of nuclear medicine and molecular imaging · 2026Article
- Scanner-integrated reconstruction versus post-processing deep learning for low-countEJNMMI physics · 2026Article
- Pathways and challenges in the clinical translational of radiopharmaceuticals for pediatric investigations.Frontiers in medicine · 2025Review
- Comparative Study on Effective Dose and Cancer Risk in Dual-tracer Hybrid Imaging - International Commission on Radiological Protection and Personalized Models.Journal of medical physicsArticle
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Authors and funding
9 authors.
Funding
Abstract
purposeLong-axial field-of-view PET scanners capture multi-organ tracer distribution with high sensitivity, enabling lower dose dynamic protocols and dual-tracer imaging for comprehensive disease characterization. However, reducing dose may compromise data quality and time-activity curve (TAC) fitting, leading to higher bias in kinetic parameters. Parametric imaging poses further challenges due to noise amplification in voxel-based modelling. We explore the potential of deep learning denoising (DL-DN) to improve quantification for low-dose dynamic PET.
methodsUsing 16 [
resultsDL-DN consistently improved image quality across all dynamic frames, systematically enhancing TAC consistency and reducing tissue-dependent bias and variability in K
conclusionThis study demonstrates that applying DL-DN trained on static [
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Registered trials
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