ArticleDiagnostics (Basel, Switzerland)2022
Medical Radiation Exposure Reduction in PET via Super-Resolution Deep Learning Model.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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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
6 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
- Development and validation of 3D super-resolution convolutional neural network forEJNMMI physics · 2025Article
- An update on recent advances in fluorescent materials for fluorescence molecular imaging: a review.RSC advances · 2025Review
- Research into super-resolution in medical imaging from 2000 to 2023: bibliometric analysis and visualization.Quantitative imaging in medicine and surgery · 2024Article
- Estimation of Left and Right Ventricular Ejection Fractions from cine-MRI Using 3D-CNN.Sensors (Basel, Switzerland) · 2023Article
- Acquisition time reduction in pediatricJournal of applied clinical medical physics · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
In positron emission tomography (PET) imaging, image quality correlates with the injected [18F]-fluorodeoxyglucose (FDG) dose and acquisition time. If image quality improves from short-acquisition PET images via the super-resolution (SR) deep learning technique, it is possible to reduce the injected FDG dose. Therefore, the aim of this study was to clarify whether the SR deep learning technique could improve the image quality of the 50%-acquisition-time image to the level of that of the 100%-acquisition-time image. One-hundred-and-eight adult patients were enrolled in this retrospective observational study. The supervised data were divided into nine subsets for nested cross-validation. The mean peak signal-to-noise ratio and structural similarity in the SR-PET image were 31.3 dB and 0.931, respectively. The mean opinion scores of the 50% PET image, SR-PET image, and 100% PET image were 3.41, 3.96, and 4.23 for the lung level, 3.31, 3.80, and 4.27 for the liver level, and 3.08, 3.67, and 3.94 for the bowel level, respectively. Thus, the SR-PET image was more similar to the 100% PET image and subjectively improved the image quality, as compared to the 50% PET image. The use of the SR deep-learning technique can reduce the injected FDG dose and thus lower radiation exposure.
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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.