ArticleEJNMMI physics2022
Deep progressive learning achieves whole-body low-dose
Article in EJNMMI physics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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20 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
- Toward an optimized PET reconstruction protocol for spinal cord: superiority of OSEM with TOF and PSF.EJNMMI physics · 2026Article
- Deep progressive learning reconstruction for fast and low-dose whole-body PET scans in an integrated PET/MR system.BMC medical imaging · 2026Article
- Optimizing acquisition time and injected dose inBMC medical informatics and decision making · 2026Article
- A Pilot Study on Quantitative Accuracy and Radiomic Feature Stability of Deep Progressive Learning Reconstruction in NSCLC PET.Journal of imaging informatics in medicine · 2026Article
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- Investigate the quantification accuracy of small lesions in oncologicalBMC medical imaging · 2026Article
- Effect of reconstruction settings on radiomics feature robustness in positron emission tomography images: a clinical study.Quantitative imaging in medicine and surgery · 2025Article
- Innovations in clinical PET image reconstruction: advances in Bayesian penalized likelihood algorithm and deep learning.Annals of nuclear medicine · 2025Review
- EnhancingCancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Comparison ofEJNMMI physics · 2025Article
- Impact of a deep progressive reconstruction algorithm on low-dose or fast-scan PET image quality and Deauville score in patients with lymphoma.EJNMMI physics · 2025Article
- Expert consensus on workflow of PET/CT with long axial field-of-view.European journal of nuclear medicine and molecular imaging · 2025Review
- Clinical evaluation of deep learning-enhanced lymphoma pet imaging with accelerated acquisition.Journal of applied clinical medical physics · 2024Article
- Stability of radiomic features from positron emission tomography images: a phantom study comparing advanced reconstruction algorithms and ordered subset expectation maximization.Physical and engineering sciences in medicine · 2024Article
- Performance and application of the total-body PET/CT scanner: a literature review.EJNMMI research · 2024Review
- Deep learning-based PET image denoising and reconstruction: a review.Radiological physics and technology · 2024Review
- Estimation of kinetic parameters in dynamic FDG PET imaging based on shortened protocols: a virtual clinical study.Physical and engineering sciences in medicine · 2024Article
- Investigation of PET image quality with acquisition time/bed and enhancement of lesion quantification accuracy through deep progressive learning.EJNMMI physics · 2024Article
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Abstract
objectivesTo validate a total-body PET-guided deep progressive learning reconstruction method (DPR) for low-dose
methodsList-mode data from the retrospective study (n = 26) were rebinned into short-duration scans and reconstructed with DPR. The standard uptake value (SUV) and tumor-to-liver ratio (TLR) in lesions and coefficient of variation (COV) in the liver in the DPR images were compared to the reference (OSEM images with full-duration data). In the prospective study, another 41 patients were injected with 1/3 of the activity based on the retrospective results. The DPR images (DPR_1/3(p)) were generated and compared with the reference (OSEM images with extended acquisition time). The SUV and COV were evaluated in three selected organs: liver, blood pool and muscle. Quantitative analyses were performed with lesion SUV and TLR, furthermore on small lesions (≤ 10 mm in diameter). Additionally, a 5-point Likert scale visual analysis was performed on the following perspectives: contrast, noise and diagnostic confidence.
resultsIn the retrospective study, the DPR with one-third duration can maintain the image quality as the reference. In the prospective study, good agreement among the SUVs was observed in all selected organs. The quantitative results showed that there was no significant difference in COV between the DPR_1/3(p) group and the reference, while the visual analysis showed no significant differences in image contrast, noise and diagnostic confidence. The lesion SUVs and TLRs in the DPR_1/3(p) group were significantly enhanced compared with the reference, even for small lesions.
conclusionsThe proposed DPR method can reduce the administered activity of
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