A probabilistic framework for uncertainty quantification in positron emission particle tracking

TL;DR

This paper introduces a Bayesian probabilistic framework for uncertainty quantification in PEPT, improving particle position reconstruction accuracy by modeling photon detection as a Poisson process and jointly inferring position and velocity.

Contribution

It develops a novel Bayesian inference approach for PEPT, explicitly modeling photon detection noise and scattering, and extends to joint position-velocity inference for dynamic particles.

Findings

Quantifies uncertainty in particle position reconstruction.

Optimizes observation time for static particles.

Enhances accuracy by joint position and velocity inference.

Abstract

Positron Emission Particle Tracking (PEPT) is an imaging method for the visualization of fluid motion, capable of reconstructing three-dimensional trajectories of small tracer particles suspended in nearly any medium, including fluids that are opaque or contained within opaque vessels. The particles are labeled radioactively, and their positions are reconstructed from the detection of pairs of back-to-back photons emitted by positron annihilation. Current reconstruction algorithms are heuristic and typically based on minimizing the distance between the particles and the so-called lines of response (LoRs) joining the detection points, while accounting for spurious LoRs generated by scattering. Here we develop a probabilistic framework for the Bayesian inference and uncertainty quantification of particle positions from PEPT data. We formulate a likelihood by describing the emission of photons and their noisy detection as a Poisson process in the space of LoRs. We derive formulas for the corresponding Poisson rate in the case of cylindrical detectors, accounting for both undetected and scattered photons. We illustrate the formulation by quantifying the uncertainty in the reconstruction of the position of a single particle on a circular path from data generated by state-of-the-art Monte Carlo simulations. The results show how the observation time $\Delta t$ can be chosen optimally to balance the need for a large number of LoRs with the requirement of small particle displacement imposed by the assumption that the particle is static over $\Delta t$. We further show how this assumption can be relaxed by inferring jointly the position and velocity of the particle, with clear benefits for the accuracy of the reconstruction.