ArticleEJNMMI research2018
Quantitative performance and optimal regularization parameter in block sequential regularized expectation maximization reconstructions in clinical
Article in EJNMMI research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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27 citing papers in PubMed.
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- Comparison of Image Quality and Quantification Parameters between Q.Clear and OSEM Reconstruction Methods on FDG-PET/CT Images in Patients with Metastatic Breast Cancer.Journal of imaging · 2023Article
- Positron Range Corrections and Denoising Techniques for Gallium-68 PET Imaging: A Literature Review.Diagnostics (Basel, Switzerland) · 2022Review
- BSREM for Brain Metastasis Detection with 18F-FDG-PET/CT in Lung Cancer Patients.Journal of digital imaging · 2022Article
- Assessing the impact of different penalty factors of the Bayesian reconstruction algorithm Q.Clear on in vivo low count kinetic analysis of [EJNMMI research · 2022Article
- The effect of Q.Clear reconstruction on quantification and spatial resolution of 18F-FDG PET in simultaneous PET/MR.EJNMMI physics · 2022Article
- The Impact of Total Variation Regularized Expectation Maximization Reconstruction onFrontiers in medicine · 2022Article
- Moving the goalposts while scoring-the dilemma posed by new PET technologies.European journal of nuclear medicine and molecular imaging · 2021Article
- New PET technologies - embracing progress and pushing the limits.European journal of nuclear medicine and molecular imaging · 2021Article
- Standardisation of conventional and advanced iterative reconstruction methods for Gallium-68 multi-centre PET-CT trials.EJNMMI physics · 2021Article
- Impact of total variation regularized expectation maximization reconstruction on the image quality ofThe British journal of radiology · 2021Article
- Comparison of Regularized Reconstruction and Ordered Subset Expectation Maximization Reconstruction in the Diagnostics of Prostate Cancer Using Digital Time-of-FlightDiagnostics (Basel, Switzerland) · 2021Article
- Can a penalized-likelihood estimation algorithm be used to reduce the injected dose or the acquisition time inEJNMMI physics · 2021Article
- Value ofEuropean journal of nuclear medicine and molecular imaging · 2021Article
- Optimization of a Bayesian penalized likelihood algorithm (Q.Clear) forEJNMMI physics · 2020Article
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7 authors.
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Abstract
backgroundIn contrast to ordered subset expectation maximization (OSEM), block sequential regularized expectation maximization (BSREM) positron emission tomography (PET) reconstruction algorithms can run until full convergence while controlling image quality and noise. Recent studies with BSREM and
resultsA total of 40 lesions were identified in the pelvic region. Background noise (SUVstd) and lesions SUVmax decreased with increasing β. Image reconstructions with β values lower than 400 have higher (p < 0.01) background noise, compared to the reference OSEM reconstructions, and are therefore less useful. Lesions with low activity on images reconstructed with β values higher than 600 have a lower (p < 0.05) SUVmax compared to the reference. These reconstructions are likely visually appealing due to the lower background noise, but the lower SUVmax could possibly render small low-uptake lesions invisible.
conclusionsIn our study, we showed that PET images reconstructed with TOF BSREM in combination with the
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