ArticleEJNMMI physics2023
A proper protocol for routine
Article in EJNMMI physics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 12 citations in OpenAlex.
- Article
- Total Body PET.Recent results in cancer research. Fortschritte der Krebsforschung. Progres dans les recherches sur le cancer · 2026Review
- Assessing small-lesion detectability and acquisition time optimisation in silicon-detector-Based PET: a phantom study.EJNMMI physics · 2025Article
- Innovations in clinical PET image reconstruction: advances in Bayesian penalized likelihood algorithm and deep learning.Annals of nuclear medicine · 2025Review
- Assessing the Potential Usefulness of FDG LAFOV-PET for Oncological Staging: An Evaluation of Lesion Number and Uptake.Cancers · 2025Article
- Leveraging small voxel with optimal acquisition time for [European journal of nuclear medicine and molecular imaging · 2025Article
- Article
- Deep learning-aided respiratory motion compensation in PET/CT: addressing motion induced resolution loss, attenuation correction artifacts and PET-CT misalignment.European journal of nuclear medicine and molecular imaging · 2024Article
- Feasibility of shortening scan duration ofEJNMMI 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
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundConventional clinical PET scanners typically have an axial field of view (AFOV) of 15-30 cm, resulting in limited coverage and relatively low photon detection efficiency. Taking advantage of the development of long-axial PET/CT, the uEXPLORER PET/CT scanner with an axial coverage of 194 cm increases the effective count rate by approximately 40 times compared to that of conventional PET scanners. Ordered subset expectation maximization (OSEM) is the most widely used iterative algorithm in PET. The major drawback of OSEM is that the iteration process must be stopped before convergence to avoid image degradation due to excessive noise. A new Bayesian penalized-likelihood iterative PET reconstruction, named HYPER iterative, was developed and is now available on the uEXPLORER total-body PET/CT, which incorporates a noise control component by using a penalty function in each iteration and finds the maximum likelihood solution through repeated iterations. To date, its impact on lesion visibility in patients with a full injected dose or half injected dose is unclear. The goal of this study was to determine a proper protocol for routine
resultsThe uEXPLORER total-body PET/CT images reconstructed using both OSEM and HYPER iterative algorithms of 20 tumour patients were retrospectively reviewed. The quality of the 5 min PET image was excellent (score 5) for all of the dose and reconstruction methods. Using the HYPER iterative method, the PET images reached excellent quality at 1 min with full-dose PET and at 2 min with half-dose PET. The PET image reached a similar excellent quality at 2 min with a full dose and at 3 min with a half dose using OSEM. The noise in the OSEM reconstruction was higher than that in the HYPER iterative. Compared to OSEM, the HYPER iterative had a slightly higher SUVmax and TBR of the lesions for large positive lesions (≥ 2 cm) (SUVmax: up to 9.03% higher in full dose and up to 12.52% higher in half dose; TBR: up to 8.69% higher in full dose and up to 23.39% higher in half dose). For small positive lesions (≤ 10 mm), the HYPER iterative had an obviously higher SUVmax and TBR of the lesions (SUVmax: up to 45.21% higher in full dose and up to 74.96% higher in half dose; TBR: up to 44.91% higher in full dose and up to 93.73% higher in half dose).
conclusionsA 1 min scan with a full dose and a 2 min scan with a half dose are optimal for clinical diagnosis using the HYPER iterative and 2 min and 3 min for OSEM. For quantification of the small lesions, HYPER iterative reconstruction is preferred.
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