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<p>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&#xa0;min; oncology: 15 and 7&#xa0;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.</p>

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Optimization of a Q.Clear PET image reconstruction based on bayesian optimization in oncology and neurology applications

  • 期刊论文

  • 发表日期：

    2026年7月14日

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  • Qariemah Azahar,
  • Hazlin Hashim,
  • Khadijah Abdul Hamid,
  • Lyu Xin,
  • Syahir Mansor

摘要

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.