Voxel-wise Bayesian Estimation for Multi-Population Positronium Lifetime Imaging

2026-08-22

AI Summary

本文提出了一种3D贝叶斯框架，用于解决多群体正电子素寿命成像中的局部寿命参数估计问题，通过体素级别的独立估计保留了局部寿命变化和统计不确定性。

Abstract

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.

Problem

• Positronium lifetime imaging
• multi-population
• voxel-wise

Innovation

• Voxel-wise Bayesian Estimation
• Multi-Population PLI
• Partial System Matrix
• Decay-Rate Posterior
• Spatially Resolved Statistical Inference