ArticleEuropean journal of nuclear medicine and molecular imaging2021
True ultra-low-dose amyloid PET/MRI enhanced with deep learning for clinical interpretation.
Article in European journal of nuclear medicine and molecular imaging, 2021. 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, 56 citations in OpenAlex.
- 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
- Technical Optimization Strategies for Amyloid PET Under Challenging Acquisition Conditions: A Comprehensive Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Simultaneous partial volume correction and denoising of brain PET images, using transformers and transfer learning.EJNMMI research · 2026Article
- SPECT and PET imaging of Alzheimer's disease revisited: from biomarkers to artificial intelligence-based prediction.Annals of nuclear medicine · 2026Review
- Score-based generative diffusion models to synthesize full-dose FDG brain PET from MRI in epilepsy patients.Frontiers in artificial intelligence · 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
- 3D full-dose brain-PET volume recovery from low-dose data through deep learning: quantitative assessment and clinical evaluation.European radiology · 2025Article
- Digital detector PET/CT increases Centiloid measures of amyloid in Alzheimer's disease: A head-to-head comparison of cameras.Journal of Alzheimer's disease : JAD · 2025Article
- Deep Convolutional Backbone Comparison for Automated PET Image Quality Assessment.IEEE transactions on radiation and plasma medical sciences · 2024Article
- Full-dose whole-body PET synthesis from low-dose PET using high-efficiency denoising diffusion probabilistic model: PET consistency model.Medical physics · 2024Article
- Intelligent ultrafast total-body PET for sedation-free pediatric [European journal of nuclear medicine and molecular imaging · 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
- Predicting FDG-PET Images From Multi-Contrast MRI Using Deep Learning in Patients With Brain Neoplasms.Journal of magnetic resonance imaging : JMRI · 2024Article
- A look at radiation detectors and their applications in medical imaging.Japanese journal of radiology · 2024Review
- A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches.ArXiv · 2024Article
- Generative Adversarial Network-Enhanced Ultra-Low-Dose [AJNR. American journal of neuroradiology · 2023Article
- International Nuclear Medicine Consensus on the Clinical Use of Amyloid Positron Emission Tomography in Alzheimer's Disease.Phenomics (Cham, Switzerland) · 2023Article
- AI Transformers for Radiation Dose Reduction in Serial Whole-Body PET Scans.Radiology. Artificial intelligence · 2023Article
- High-quality PET image synthesis from ultra-low-dose PET/MRI using bi-task deep learning.Quantitative imaging in medicine and surgery · 2022Article
Corrections and comments
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Authors and funding
13 authors at 2 institutions in 1 country.
Funding
Abstract
purposeWhile sampled or short-frame realizations have shown the potential power of deep learning to reduce radiation dose for PET images, evidence in true injected ultra-low-dose cases is lacking. Therefore, we evaluated deep learning enhancement using a significantly reduced injected radiotracer protocol for amyloid PET/MRI.
methodsEighteen participants underwent two separate
resultsThe deep learning-enhanced PET images showed marked improvement on all quality metrics compared with the low-dose images as well as having generally similar regional CVs as the standard-dose. All enhanced images were non-inferior to their standard-dose counterparts. Accuracy for amyloid status was high (97.2% and 91.7% for images enhanced from injected and sampled ultra-low-dose data, respectively) which was similar to intra-reader reproducibility of standard-dose images (98.6%).
conclusionDeep learning methods can synthesize diagnostic-quality PET images from ultra-low injected dose simultaneous PET/MRI data, demonstrating the general validity of sampled realizations and the potential to reduce dose significantly for amyloid imaging.
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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.