ArticleNuclear medicine and molecular imaging2019
How Do the More Recent Reconstruction Algorithms Affect the Interpretation Criteria of PET/CT Images?
Article in Nuclear medicine and molecular imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 31 citations in OpenAlex.
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- A retrospective evaluation of Bayesian-penalized likelihood reconstruction for [Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023Article
- Rapid Whole-Body FDG PET/MRI in Oncology Patients: Utility of Combining Bayesian Penalised Likelihood PET Reconstruction and Abbreviated MRI.Diagnostics (Basel, Switzerland) · 2023Article
- Enhancement ofDiagnostics (Basel, Switzerland) · 2022Article
- Can Q.Clear reconstruction be used to improve [68 Ga]Ga-DOTANOC PET/CT image quality in overweight NEN patients?European journal of nuclear medicine and molecular imaging · 2022Article
- Small lesion depiction and quantification accuracy of oncologicalEJNMMI physics · 2022Article
- PET/CT Radiomic Features: A Potential Biomarker for EGFR Mutation Status and Survival Outcome Prediction in NSCLC Patients Treated With TKIs.Frontiers in oncology · 2022Article
- Radiomics in Gastric Cancer: First Clinical Investigation to Predict Lymph Vascular Invasion and Survival Outcome UsingFrontiers in oncology · 2022Article
- Changes of [BMC medical imaging · 2021Article
- Head-to-head comparison of a Si-photomultiplier-based and a conventional photomultiplier-based PET-CT system.EJNMMI physics · 2021Article
- Can a penalized-likelihood estimation algorithm be used to reduce the injected dose or the acquisition time inEJNMMI physics · 2021Article
- Impact of the Q.Clear reconstruction algorithm on the interpretation of PET/CT images in patients with lymphoma.EJNMMI research · 2020Article
- Ordered subset expectation maximisation vs Bayesian penalised likelihood reconstruction algorithm in 18F-PSMA-1007 PET/CT.Annals of nuclear medicine · 2020Article
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6 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
purposeRecently, a new Bayesian Penalized Likelihood (BPL) Reconstruction Algorithm was introduced by GE Healthcare, Q.Clear; it promises to provide better PET image resolution compared to the widely used Ordered Subset Expectation Maximization (OSEM). The aim of this study is to compare the performance of these two algorithms on several types of findings, in terms of image quality, lesion detectability, sensitivity, and specificity.
methodsBetween September 6th 2017 and July 31st 2018, 663 whole body 18F-FDG PET/CT scans were performed at the Nuclear Medicine Department of S. Martino Hospital (Belluno, Italy). Based on the availability of clinical/radiological follow-up data, 240 scans were retrospectively reviewed. For each scan, a hypermetabolic finding was selected, reporting both for OSEM and Q.Clear: SUVmax and SUVmean values of the finding, the liver and the background close to the finding; size of the finding; percentage variations of SUVmax and SUVmean. Each finding was subsequently correlated with clinical and radiological follow-up, to define its benign/malignant nature.
resultsOverall, Q.Clear improved the SUV values in each scan, especially in small findings (< 10 mm), high SUVmax values (≥ 10), and medium/low backgrounds. Furthermore, Q.Clear amplifies the signal of hypermetabolic findings without modifying the background signal, which leads to an increase in signal-to-noise ratio, improving overall image quality. Finally, Q.Clear did not affect PET sensitivity or specificity, in terms of number of reported findings and characterization of their nature.
conclusionsQ.Clear is an iterative algorithm that improves significantly the quality of PET images compared to OSEM, increasing the SUVmax of findings (in particular for small findings) and the signal-to-noise ratio. However, due to the intrinsic characteristics of this algorithm, it will be necessary to adapt and/or modify the current interpretative criteria based of quantitative evaluation, to avoid an overestimation of the disease burden.
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