ArticlePhysical and engineering sciences in medicine2026
Optimization of a Q.Clear PET image reconstruction based on bayesian optimization in oncology and neurology applications.
Article in Physical and engineering sciences in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
Bayesian Penalized Likelihood (BPL) reconstruction can improve image quality and quantitative accuracy in PET/CT imaging. This study aimed to determine the optimal Beta (β)-factor in Q.Clear reconstruction using Bayesian Optimization (BO) through phantoms and clinical evaluation. NEMA IQ and Hoffman Brain phantoms, along with clinical PET/CT images, were analyzed. Phantom images were reconstructed using β-factors (50-1000) across different acquisition times. Recovery coefficient (RC%) and contrast-to-noise ratio (CNR) were calculated. Clinical evaluation included one neurology and one oncology case as verification data, reconstructed using OSEM and Q.Clear at β-factors (100-1000), with full and reduced reconstruction times (neurology: 8 and 2 min; oncology: 15 and 7 min), respectively. Visual assessment was independently performed by two nuclear medicine physicians, and inter-rater agreement was analyzed using Krippendorff's alpha. In the NEMA IQ phantom, increasing β-factor resulted in decreased RC% but increased CNR. In the Hoffman Brain phantom, cortical regions showed stable RC% from mid to high β-factors, whereas subcortical regions were more sensitive, showing reduced RC%. CNR improved with higher β-factors and longer acquisition times. BO analysis indicates that smaller spheres achieved optimal performance at lower β-factors and longer acquisition times, while larger spheres tolerated higher β-factors (600-800). Optimal β-factors for cortical and subcortical brain regions were (400-600) and (100-300), respectively. Clinically, the highest scores were observed for β-factors (300-1000) in oncology, and (500-900) in neurology. Higher β-factors improved RC% and CNR stability in phantom studies. BO identified (500-900) as the optimal β-factors, consistent with clinical findings demonstrating high visual scores and strong inter-rater agreement, supporting its reliability of BO-optimized β-factors for PET/CT reconstruction.
Indexed as
Identifiers
42446842What 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.