ArticleEuropean journal of nuclear medicine and molecular imaging2022
A cross-scanner and cross-tracer deep learning method for the recovery of standard-dose imaging quality from low-dose PET.
Article 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 35 papers, 1 of them a synthesis that pooled it.
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Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning-based techniques for estimating high-quality full-dose positron emission tomography images from low-dose scans: a systematic review.BMC medical imaging · 2024Pooled it
- LeqMod: Adaptable Lesion-Quantification-Consistent Modulation for Deep Learning Low-Count PET Image Denoising.IEEE transactions on medical imaging · 2026Article
- Anatomically and metabolically informed diffusion for unified denoising and segmentation in low-count PET imaging.Medical image analysis · 2026Article
- Data-efficient generalization of AI transformers for noise reduction in ultra-fast lung PET scans.European journal of nuclear medicine and molecular imaging · 2025Article
- Non-parametric Bayesian deep learning approach for whole-body low-dose PET reconstruction and uncertainty assessment.Medical & biological engineering & computing · 2025Article
- A deep learning method for the recovery of standard-dose imaging quality from ultra-low-dose PET on wavelet domain.European journal of nuclear medicine and molecular imaging · 2025Article
- A digital twin of the Biograph Vision Quadra long axial field of view PET/CT: Monte Carlo simulation and image reconstruction framework.EJNMMI physics · 2025Article
- 3D full-dose brain-PET volume recovery from low-dose data through deep learning: quantitative assessment and clinical evaluation.European radiology · 2025Article
- Dopaminergic PET to SPECT domain adaptation: a cycle GAN translation approach.European journal of nuclear medicine and molecular imaging · 2025Article
- Long-axial field-of-view PET/CT improves radiomics feature reliability.European journal of nuclear medicine and molecular imaging · 2025Article
- The impact of long axial field of view (LAFOV) PET on oncologic imaging.European journal of radiology · 2025Review
- Generative artificial intelligence and ethical considerations in health care: a scoping review and ethics checklist.The Lancet. Digital health · 2024Article
- Sedation-free pediatric [European journal of nuclear medicine and molecular imaging · 2024Article
- IE-CycleGAN: improved cycle consistent adversarial network for unpaired PET image enhancement.European journal of nuclear medicine and molecular imaging · 2024Article
- Clinical evaluation of deep learning-enhanced lymphoma pet imaging with accelerated acquisition.Journal of applied clinical medical physics · 2024Article
- Full-dose whole-body PET synthesis from low-dose PET using high-efficiency denoising diffusion probabilistic model: PET consistency model.Medical physics · 2024Article
- The clinical acceptability of short versus long duration acquisitions for head and neck cancer using long-axial field-of-view PET/CT: a retrospective evaluation.European journal of nuclear medicine and molecular imaging · 2024Article
- Unified Noise-aware Network for Low-count PET Denoising with Varying Count Levels.IEEE transactions on radiation and plasma medical sciences · 2024Article
- A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches.IEEE transactions on radiation and plasma medical sciences · 2024Article
- Deep learning-based PET image denoising and reconstruction: a review.Radiological physics and technology · 2024Review
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
purposeA critical bottleneck for the credibility of artificial intelligence (AI) is replicating the results in the diversity of clinical practice. We aimed to develop an AI that can be independently applied to recover high-quality imaging from low-dose scans on different scanners and tracers.
methodsBrain [
resultsThe improvement achieved by AI recovery significantly correlated with the baseline image quality indicated by structural similarity index measurement (SSIM) (r = -0.71, p < 0.05) and normalized dose acquisition (r = -0.60, p < 0.05). Our cross-scanner and cross-tracer AI methodology showed utility based on both physical and clinical image assessment (p < 0.05).
conclusionThe deep learning development for extensible application on unknown scanners and tracers may improve the trustworthiness and clinical acceptability of AI-based dose reduction.
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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.