ArticleEJNMMI physics2020
Evaluation of block-sequential regularized expectation maximization reconstruction of
Article in EJNMMI physics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 33 citations in OpenAlex.
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- Impact of patient size on image quality of OSEM3D and BSREM reconstructions in [EJNMMI physics · 2025Article
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- The effects of various penalty parameter values in Q.Clear algorithm for rectal cancer detection onEJNMMI physics · 2023Article
- Phantom and clinical evaluation of Block Sequential Regularized Expectation Maximization (BSREM) reconstruction algorithm in 68Ga-PSMA PET-CT studies.Physical and engineering sciences in medicine · 2023Article
- Positron Range Corrections and Denoising Techniques for Gallium-68 PET Imaging: A Literature Review.Diagnostics (Basel, Switzerland) · 2022Review
- Phantom-based acquisition time and image reconstruction parameter optimisation for oncologic FDG PET/CT examinations using a digital system.BMC cancer · 2022Article
- BSREM for Brain Metastasis Detection with 18F-FDG-PET/CT in Lung Cancer Patients.Journal of digital imaging · 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
- 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
- Impact of segmentation and discretization on radiomic features inEJNMMI physics · 2021Article
- Can a penalized-likelihood estimation algorithm be used to reduce the injected dose or the acquisition time inEJNMMI physics · 2021Article
- Image reconstruction methods affect software-aided assessment of pathologies of [NeuroImage. Clinical · 2020Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundBlock-sequential regularized expectation maximization (BSREM) is a fully convergent iterative image reconstruction algorithm. We hypothesize that tracers with different distribution patterns will result in different optimal settings for the BSREM algorithm. The aim of this study was to evaluate the image quality with variations in the applied β-value and acquisition time for three positron emission tomography (PET) tracers. NEMA image quality phantom measurements and clinical whole-body digital time-of-flight (TOF) PET/computed tomography (CT) examinations with
resultsThe highest β-value resulted in the lowest level of noise, which in turn resulted in the highest SNR and lowest SBR. Noise levels equal to or lower than those of OSEM were found with β-values equal to or higher than 400, 533, and 267 for
conclusionsWithin tracer-specific ranges of β-values, BSREM reconstruction resulted in increased SNR and SBR with respect to conventional OSEM reconstruction. Similar SNR, SBR, and noise levels could be attained with BSREM at relatively shorter acquisition times or, alternatively, lower administered dosages, compared to those attained with OSEM.
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