New algorithm tracks multiple particles in action

Positron emission particle tracking (PEPT) can help scientists visualize particle movement in three dimensions. It relies on the detection of gamma-rays emitted by radioactive particles as a result of positron annihilation. Until recently, PEPT has been limited to tracking single particles. Manger et al. developed an algorithm that is able to track multiple particles simultaneously.

The team found their method successfully reconstructed the positions of 80 particles in simulations, and up to 10 particles during experiments, which were limited by their detector’s data acquisition rates.

“The performance of algorithms tracking multiple particles typically degrade quickly as the number of particles increases, as these algorithms aren’t designed to handle scattered and random lines of response,” author Sam Manger said. “Our expectation–maximization technique includes outlier lines of response as a component in the model – therefore it is very good at discarding lines of response that lie far away from particles, reducing their detrimental effect on the tracked location.”

The algorithm works by determining the locations of radioactive particles that best explain the detected gamma rays. Advanced versions estimate the most likely velocity and acceleration of particles as well as their position, which improves accuracy and reduces the computation required to characterize particles in fluid flows.

The team plans to continue improving and testing their algorithm.

“Work is underway to evaluate the uncertainties in PEPT from first principles in order to extend and improve the model behind our algorithm,” Manger said.

Source: “An expectation–maximization algorithm for positron emission particle tracking,” by Sam Manger, Antoine Renaud, and Jacques Vanneste, Review of Scientific Instruments (2021). The article can be accessed at https://doi.org/10.1063/5.0053545 .