发表机构
Université de Sherbrooke; Interdisciplinary Institute for Technological Innovation; Sherbrooke Molecular Imaging Center of CRCHUS; Institut Fresnel; Aix Marseille Univ; Centrale Med; Imaging Research & Technology (IR&T) Inc.（谢布鲁克大学; 跨学科技术创新研究所; CRCHUS谢布鲁克分子成像中心; 弗雷内尔研究所; 艾克斯-马赛大学; 中央医学院; 成像研究与技术公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对超高分辨率PET的晶间散射难题，提出前馈神经网络优化晶间散射恢复，在保持亚毫米空间分辨率的同时提升灵敏度，可缩短扫描时间并降低辐射剂量。
AI 中文摘要
晶间散射（ICS）事件是超高分辨率正电子发射断层成像（UHR-PET）面临的重大挑战，尤其当探测器晶体尺寸变小且读出电路分段化程度提高时更为突出。现有方法要么拒绝这些事件，降低了灵敏度；要么接受这些事件但采用次优定位算法，导致图像分辨率下降。我们提出一种前馈神经网络，通过推断首次康普顿相互作用所属的响应线来优化ICS事件恢复。该方法已通过蒙特卡罗模拟和基于全像素化LabPET-II的临床前及脑部UHR-PET实验数据验证。结果显示，与传统方法相比，我们的方法在保持亚毫米空间分辨率（低至1.6 mm）的同时，灵敏度提升了70%至106%。这种ICS恢复方法是一种有效的解决方案，可弥补UHR-PET中小型像素化探测器检测效率较低的问题，能够缩短扫描时间、降低辐射剂量，同时在很大程度上保持图像质量。
英文摘要
Inter-crystal scatter (ICS) events pose a significant challenge in ultrahigh- resolution positron emission tomography (UHR-PET), especially as detector crystals become smaller and their readouts increasingly segmented. Current approaches either reject these events, reducing sensitivity, or accept them with suboptimal positioning algorithms, degrading image resolution. We present a feed forward neural network to optimize ICS event recovery by inferring the line-of-response belonging to the first Compton interaction. Our approach was validated using both Monte Carlo simulations and experimental data from the fully pixelated LabPET-IIbased preclinical and brain UHR-PET scanners.Results demonstrate a 70% to 106% increase in sensitivity while preserving sub-millimeter spatial resolvability (down to 1.6 mm) compared to conventional methods. This ICS recovery approach is an effective solution that compensates for the lower detection efficiency of small, pixelated detectors in UHR-PET, enabling reduced scan times and lower radiation doses while largely preserving image quality.
Comments19 pages, to b published in Physic in Medecine and Biology
