Sykes Jack A, Nicuşan Andrei L, Werner Dominik, Herald Matthew T, Weston Daniel, Wheldon Tzany Kokalova, Windows-Yule Christopher R K
School of Physics and Astronomy, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
School of Chemical Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
Sci Rep. 2025 Apr 19;15(1):13599. doi: 10.1038/s41598-025-97175-0.
Positron emission particle tracking (PEPT) is an advanced imaging technique that accurately tracks the three-dimensional spatial coordinates of a radioactively-labelled particle with sub-millimetre and sub-millisecond precision. By detecting back-to-back 511 keV gamma rays from positron-electron annihilation coincidence events, PEPT can locate particles within highly dense, opaque systems such as fluidised beds, rotating drums, and mills. Despite the progress made in enhancing the precision and accuracy of PEPT, simultaneous multiple particle tracking remains a significant challenge, particularly in high-noise environments. This paper introduces T-PEPT, a novel algorithm that leverages topological data analysis-a relatively new field of applied mathematics that explores the underlying 'shape' of data through techniques like persistence homology. By creating simplicial complexes and applying persistence homology to PEPT point data, T-PEPT demonstrates highly effective performance in multiple-particle tracking, especially in scenarios with high noise. When benchmarked against existing PEPT algorithms using a widely recognised standard framework, T-PEPT consistently maintains sub-millimetre spatial and sub-millisecond temporal precision in nearly all cases, demonstrating its robustness and accuracy. For Data availability for T-PEPT, please use the GitHub repository: https://github.com/uob-positron-imaging-centre/pept .
正电子发射粒子跟踪（PEPT）是一种先进的成像技术，能够以亚毫米和亚毫秒的精度精确跟踪放射性标记粒子的三维空间坐标。通过检测正电子 - 电子湮灭符合事件产生的背对背511 keV伽马射线，PEPT可以在诸如流化床、转鼓和磨机等高密度、不透明系统中定位粒子。尽管在提高PEPT的精度和准确性方面取得了进展，但同时进行多粒子跟踪仍然是一项重大挑战，尤其是在高噪声环境中。本文介绍了T - PEPT，这是一种利用拓扑数据分析的新颖算法，拓扑数据分析是应用数学中一个相对较新的领域，它通过持久同调等技术探索数据的潜在“形状”。通过创建单纯复形并将持久同调应用于PEPT点数据，T - PEPT在多粒子跟踪中表现出高效性能，特别是在高噪声场景中。当使用广泛认可的标准框架与现有的PEPT算法进行基准测试时，T - PEPT在几乎所有情况下都始终保持亚毫米的空间精度和亚毫秒的时间精度，证明了其稳健性和准确性。有关T - PEPT的数据可用性，请使用GitHub存储库：https://github.com/uob-positron-imaging-centre/pept 。
