ArticleQuantitative imaging in medicine and surgery2023
Comparative study of the quantitative accuracy of oncological PET imaging based on deep learning methods.
Article in Quantitative imaging in medicine and surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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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
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 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
- Deep progressive learning reconstruction for fast and low-dose whole-body PET scans in an integrated PET/MR system.BMC medical imaging · 2026Article
- EnhancingCancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Deep learning-based PET image denoising and reconstruction: a review.Radiological physics and technology · 2024Review
- Investigation of PET image quality with acquisition time/bed and enhancement of lesion quantification accuracy through deep progressive learning.EJNMMI physics · 2024Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: [ Methods: A total of 311 tumor patients who underwent Results: There was high consistency in image quality scores among all groups [Kappa =0.719, 95% confidence interval (CI): 0.697-0.741, P<0.001]. There were 264 cases (3D Unet-15s), 311 cases (3D Unet-30s), 89 cases (P2P-15s) and 247 cases (P2P-30s) with image quality score ≥3, respectively. There was significant difference in the score composition among all groups (χ Conclusions: Both GAN and CNN can suppress image noise to varying degrees and improve image quality. However, when 3D Unet reduces the noise of tumor lesions, it can improve the CNR of tumor lesions. Moreover, quantitative parameters of tumor tissue are similar to those under the standard acquisition protocol, which can meet the needs of clinical diagnosis.
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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.