多群体正电子素寿命成像的体素级贝叶斯估计
Voxel-wise Bayesian Estimation for Multi-Population Positronium Lifetime Imaging
AI总结：
针对现有正电子素寿命成像（PLI）忽略同一位置多寿命群体的问题，提出三维群体特异性贝叶斯框架，通过体素级估计实现高效的多群体PLI，保留局部变异与不确定性，模拟和实验验证其有效性。
AI中文摘要：
正电子素寿命成像（PLI）提供了超越常规活性成像的局部湮灭环境数据。然而，现有方法通常在预定义区域上估计寿命参数，忽略了同一位置内的多个寿命群体。我们提出了一种用于快速体素级PLI的三维、群体特异性贝叶斯框架。部分系统矩阵描述了检测事件的空间概率，而测量的寿命则对慢、快和噪声群体提供软分配。这些事件责任被用于独立估计每个体素的衰变率后验，保留局部寿命变化和统计不确定性。在模拟中，我们的公式恢复了空间变化的慢群体衰变率和共同的快群体衰变率，而单群体模型则产生系统偏差。慢群体的两倍标准差覆盖率范围为93.8%至98.1%。使用Siemens Biograph Vision Quadra扫描仪的124I三重符合数据进行实验验证，生成了铝、镍、铜和石英的单独慢、快群体图。长寿命的石英组分与正正电子素匹配，而快群体显示出金属间的材料依赖性差异。快群体的覆盖率较低（69.1%），表明不确定性被低估。该方法效率极高，在单个CPU核心上每个群体仅需几秒到几分钟。此框架提供了快速、群体特异性的PLI，带有贝叶斯不确定性量化，使空间分辨的统计推断对于体积应用而言可行且实用。
英文摘要：
Positronium lifetime imaging (PLI) provides local annihilation environment data beyond conventional activity imaging. Existing approaches, however, often estimate lifetime parameters over predefined regions, neglecting multiple lifetime populations within the same location. We present a 3D, population-specific Bayesian framework for fast voxel-wise PLI. A partial system matrix describes the spatial probability of detected events, while measured lifetimes provide soft assignments to slow, fast, and noise populations. These event responsibilities are used to estimate a decay-rate posterior independently for each voxel, preserving local lifetime variation and statistical uncertainty. In simulations, our formulation recovered spatially varying slow-population decay rates and a common fast-population rate, whereas a single-population model produced systematic bias. Slow-population two-standard-deviation coverage ranged from 93.8% to 98.1%. Experimental validation using 124I triple-coincidence data from a Siemens Biograph Vision Quadra scanner produced separate slow- and fast-population maps for aluminum, nickel, copper, and quartz. The long-lived quartz component matched ortho-positronium, while the fast population showed material-dependent differences among metals. Fast-population coverage was lower (69.1%), indicating underestimated uncertainty. The method is highly efficient, requiring only seconds to minutes per population on a single CPU core. This framework provides fast, population-specific PLI with Bayesian uncertainty quantification, making spatially resolved statistical inference feasible and practical for volumetric applications.
