ArticleEJNMMI physics2023
Optimization of Q.Clear reconstruction for dynamic
Article in EJNMMI physics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03951142 (Imaging Perfusion Restrictions From Extracellular Solid Stress - An Open-label Losartan Study), which is not on this map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Imaging Perfusion Restrictions From Extracellular Solid Stress - An Open-label Losartan Study
Who cites it
10 citing papers in PubMed.
- Optimization of a Q.Clear PET image reconstruction based on bayesian optimization in oncology and neurology applications.Physical and engineering sciences in medicine · 2026Article
- Optimisation of Q.Clear (BSREM) Reconstruction Parameters forBioengineering (Basel, Switzerland) · 2026Article
- QuantitativeEJNMMI physics · 2026Article
- Renal dynamic scintigraphy using 3D-ring CZT cameras: comparison with NaI(Tl).EJNMMI research · 2025Article
- Does Q.Clear Processing Change PET Ratios? Quantitative Evidence Using BTXBrain-DAT.Brain sciences · 2025Article
- Improving image reconstruction to quantify dynamic whole-body PET/CT: Q.Clear versus OSEM.EJNMMI physics · 2025Article
- Phantom-based investigation of block sequential regularised expectation maximisation (BSREM) reconstruction for zirconium-89 PET-CT for varied count levels.EJNMMI physics · 2025Article
- Quantification of FDG in the spinal cord using PET/MRI.Frontiers in nuclear medicine · 2025Article
- MR-guided reconstruction of PET data in spinal cord PET/MRI.Frontiers in nuclear medicine · 2025Article
- β1600 Q.Clear Digital Reconstruction of [Journal of clinical medicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundQ.Clear, a Bayesian penalized likelihood reconstruction algorithm, has shown high potential in improving quantitation accuracy in PET systems. The Q.Clear algorithm controls noise during the iterative reconstruction through a β penalization factor. This study aimed to determine the optimal β-factor for accurate quantitation of dynamic PET scans.
methodsA Flangeless Esser PET Phantom with eight hollow spheres (4-25 mm) was scanned on a GE Discovery MI PET/CT system. Data were reconstructed into five sets of variable acquisition times using Q.Clear with 18 different β-factors ranging from 100 to 3500. The recovery coefficient (RC), coefficient of variation (CV
resultsIn general, RC and CV
conclusionIn a complex dynamic dataset with variable acquisition times, the optimal β-factor provides a balance between accuracy and precision. Based on our results, we suggest a β-factor of 300-500 for quantitation of small structures with dynamic PET imaging, while large structures may benefit from higher β-factors.
trial registrationClinicaltrials.gov, NCT03951142. Registered 5 October 2019, https://clinicaltrials.gov/ct2/show/NCT03951142 . EudraCT no 2018-003229-27. Registered 26 February 2019, https://www.clinicaltrialsregister.eu/ctr-search/trial/2018-003229-27/NO .
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.