ArticleNPJ digital medicine2021
Low-count whole-body PET with deep learning in a multicenter and externally validated study.
Article in NPJ digital medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 50 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
50 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
- Emerging frontiers and challenges of artificial intelligence in PSMA-PET imaging: pioneering a new chapter in prostate cancer care.Japanese journal of radiology · 2026Review
- Immuno-imaging in solid organ transplantation.American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · 2026Review
- Review
- An AI-Based Solution for Denoising Fast-Acquisition [Journal of imaging informatics in medicine · 2026Article
- Pair production tomography enables imaging of MeV-scale gamma-emitting theranostic radionuclides.Research square · 2026Article
- SPECT and PET imaging of Alzheimer's disease revisited: from biomarkers to artificial intelligence-based prediction.Annals of nuclear medicine · 2026Review
- Accelerating imaging: deep learning for enhancedJapanese journal of radiology · 2026Article
- Artificial Intelligence and Digital Technology in Cardiovascular Imaging: A Narrative Review.Biotech (Basel (Switzerland)) · 2026Review
- Low-count whole-body PET denoising with deep learning in a multicenter, multi-tracer and externally validated study.European journal of nuclear medicine and molecular imaging · 2026Article
- Scanner-integrated reconstruction versus post-processing deep learning for low-countEJNMMI physics · 2026Article
- Article
- AI-enabled multimodal neuroimaging for neurotransmitter mapping in normal aging and age-related disease.Frontiers in aging neuroscience · 2026Review
- Clinical validation of a deep learning model for low-count PET image enhancement.European journal of nuclear medicine and molecular imaging · 2025Article
- Nanoradiopharmaceuticals: Design Principles, Radiolabeling Strategies, and Biomedicine Applications.Pharmaceutics · 2025Review
- Incorporating Radiologist Knowledge Into MRI Quality Metrics for Machine Learning Using Rank-Based Ratings.Journal of magnetic resonance imaging : JMRI · 2025Article
- Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis.Genomics, proteomics & bioinformatics · 2025Review
- External phantom-based validation of a deep-learning network trained for upscaling of digital low count PET data.EJNMMI physics · 2025Article
- Staging of prostate Cancer with ultra-fast PSMA-PET scans enhanced by AI.European journal of nuclear medicine and molecular imaging · 2025Article
- Ultra-low dose immunoPET usingFrontiers in medicine · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
More widespread use of positron emission tomography (PET) imaging is limited by its high cost and radiation dose. Reductions in PET scan time or radiotracer dosage typically degrade diagnostic image quality (DIQ). Deep-learning-based reconstruction may improve DIQ, but such methods have not been clinically evaluated in a realistic multicenter, multivendor environment. In this study, we evaluated the performance and generalizability of a deep-learning-based image-quality enhancement algorithm applied to fourfold reduced-count whole-body PET in a realistic clinical oncologic imaging environment with multiple blinded readers, institutions, and scanner types. We demonstrate that the low-count-enhanced scans were noninferior to the standard scans in DIQ (p < 0.05) and overall diagnostic confidence (p < 0.001) independent of the underlying PET scanner used. Lesion detection for the low-count-enhanced scans had a high patient-level sensitivity of 0.94 (0.83-0.99) and specificity of 0.98 (0.95-0.99). Interscan kappa agreement of 0.85 was comparable to intrareader (0.88) and pairwise inter-reader agreements (maximum of 0.72). SUV quantification was comparable in the reference regions and lesions (lowest p-value=0.59) and had high correlation (lowest CCC = 0.94). Thus, we demonstrated that deep learning can be used to restore diagnostic image quality and maintain SUV accuracy for fourfold reduced-count PET scans, with interscan variations in lesion depiction, lower than intra- and interreader variations. This method generalized to an external validation set of clinical patients from multiple institutions and scanner types. Overall, this method may enable either dose or exam-duration reduction, increasing safety and lowering the cost of PET imaging.
Identifiers
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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.