ArticleEuropean journal of nuclear medicine and molecular imaging2020
Generalization of deep learning models for ultra-low-count amyloid PET/MRI using transfer learning.
Article in European journal of nuclear medicine and molecular imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 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
- Simultaneous partial volume correction and denoising of brain PET images, using transformers and transfer learning.EJNMMI research · 2026Article
- AI-enabled multimodal neuroimaging for neurotransmitter mapping in normal aging and age-related disease.Frontiers in aging neuroscience · 2026Review
- Artificial intelligence for radiopharmaceutical and molecular imaging.Acta pharmaceutica Sinica. B · 2025Review
- Data-efficient generalization of AI transformers for noise reduction in ultra-fast lung PET scans.European journal of nuclear medicine and molecular imaging · 2025Article
- 3D full-dose brain-PET volume recovery from low-dose data through deep learning: quantitative assessment and clinical evaluation.European radiology · 2025Article
- Artificial Intelligence-Based Methodologies for Early Diagnostic Precision and Personalized Therapeutic Strategies in Neuro-Ophthalmic and Neurodegenerative Pathologies.Brain sciences · 2024Review
- Comparison of the Accuracy of a Deep Learning Method for Lesion Detection in PET/CT and PET/MRI Images.Molecular imaging and biology · 2024Article
- Deep learning based bilateral filtering for edge-preserving denoising of respiratory-gated PET.EJNMMI physics · 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
- Perspectives of the European Association of Nuclear Medicine on the role of artificial intelligence (AI) in molecular brain imaging.European journal of nuclear medicine and molecular imaging · 2024Article
- A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches.ArXiv · 2024Article
- PET image denoising based on denoising diffusion probabilistic model.European journal of nuclear medicine and molecular imaging · 2024Article
- Application of a validated prostate MRI deep learning system to independent same-vendor multi-institutional data: demonstration of transferability.European radiology · 2023Article
- International Nuclear Medicine Consensus on the Clinical Use of Amyloid Positron Emission Tomography in Alzheimer's Disease.Phenomics (Cham, Switzerland) · 2023Article
- Comparative study of the quantitative accuracy of oncological PET imaging based on deep learning methods.Quantitative imaging in medicine and surgery · 2023Article
- AI Transformers for Radiation Dose Reduction in Serial Whole-Body PET Scans.Radiology. Artificial intelligence · 2023Article
- Ultra-low-dose in brain 18F-FDG PET/MRI in clinical settings.Scientific reports · 2022Article
- A personalized deep learning denoising strategy for low-count PET images.Physics in medicine and biology · 2022Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
purposeWe aimed to evaluate the performance of deep learning-based generalization of ultra-low-count amyloid PET/MRI enhancement when applied to studies acquired with different scanning hardware and protocols.
methodsEighty simultaneous [
resultsAll network-synthesized images had reduced noise than their ultra-low-count reconstructions. Quantitatively, image metrics improved the most using method B, where SUVRs had the least variability from the ground-truth and the highest effect size to differentiate between positive and negative images. Method A images had lower accuracy and image quality than other methods; images synthesized from methods B-D scored similarly or better than the ground-truth images.
conclusionsDeep learning can successfully produce diagnostic amyloid PET images from short frame reconstructions. Data bias should be considered when applying pre-trained deep ultra-low-count amyloid PET/MRI networks for generalization.
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