ArticleEJNMMI physics2021
Performance evaluation of the Q.Clear reconstruction framework versus conventional reconstruction algorithms for quantitative brain PET-MR studies.
Article in EJNMMI physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
- Reconstruction-dependent differences in brain [18F]FDG PET: a large-scale same-acquisition comparison of Q.Clear and VUE Point FX.Annals of nuclear medicine · 2026Article
- Optimization of a Q.Clear PET image reconstruction based on bayesian optimization in oncology and neurology applications.Physical and engineering sciences in medicine · 2026Article
- A comparison of three-dimensional-ring CZT and NaI(Tl) single-photon emission computed tomography systems usingQuantitative imaging in medicine and surgery · 2026Article
- Q.Clear reconstruction shortening the scanning time forEJNMMI physics · 2026Article
- A Novel Method in PET Image Reconstruction Using MRI Anatomical Priors.IEEE transactions on radiation and plasma medical sciences · 2025Article
- Qualitative and quantitative analysis of reduced bed position acquisition time on FDG PET image quality.Nuclear medicine communications · 2025Article
- Improving image reconstruction to quantify dynamic whole-body PET/CT: Q.Clear versus OSEM.EJNMMI physics · 2025Article
- Comparative evaluation of Ordered Subset Expectation Maximization and Bayesian Penalized Likelihood algorithms for PET/CT image reconstruction in various malignancies usingFrontiers in oncology · 2025Article
- Y-90 PET/MR imaging optimization with a Bayesian penalized likelihood reconstruction algorithm.Physical and engineering sciences in medicine · 2024Article
- Bias and precision of SPECT-basedEJNMMI physics · 2024Article
- β1600 Q.Clear Digital Reconstruction of [Journal of clinical medicine · 2024Article
- AI for PET image reconstruction.The British journal of radiology · 2023Review
- 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
- A review of harmonization strategies for quantitative PET.Annals of nuclear medicine · 2023Review
- 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
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3 authors.
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Abstract
backgroundQ.Clear is a Bayesian penalized likelihood (BPL) reconstruction algorithm that presents improvements in signal-to-noise ratio (SNR) in clinical positron emission tomography (PET) scans. Brain studies in research require a reconstruction that provides a good spatial resolution and accentuates contrast features however, filtered back-projection (FBP) reconstruction is not available on GE SIGNA PET-Magnetic Resonance (PET-MR) and studies have been reconstructed with an ordered subset expectation maximization (OSEM) algorithm. This study aims to propose a strategy to approximate brain PET quantitative outcomes obtained from images reconstructed with Q.Clear versus traditional FBP and OSEM.
methodsContrast recovery and background variability were investigated with the National Electrical Manufacturers Association (NEMA) Image Quality (IQ) phantom. Resolution, axial uniformity and SNR were investigated using the Hoffman phantom. Both phantoms were scanned on a Siemens Biograph 6 TruePoint PET-Computed Tomography (CT) and a General Electric SIGNA PET-MR, for FBP, OSEM and Q.Clear. Differences between the metrics obtained with Q.Clear with different β values and FBP obtained on the PET-CT were determined.
resultsFor in plane and axial resolution, Q.Clear with low β values presented the best results, whereas for SNR Q.Clear with higher β gave the best results. The uniformity results are greatly impacted by the β value, where β < 600 can yield worse uniformity results compared with the FBP reconstruction.
conclusionThis study shows that Q.Clear improves contrast recovery and provides better resolution and SNR, in comparison to OSEM, on the PET-MR. When using low β values, Q.Clear can provide similar results to the ones obtained with traditional FBP reconstruction, suggesting it can be used for quantitative brain PET kinetic modelling studies.
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