Nicuşan A L, Windows-Yule C R K
School of Chemical Engineering, The University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom.
Rev Sci Instrum. 2020 Jan 1;91(1):013329. doi: 10.1063/1.5129251.
We introduce a new approach to positron emission particle tracking based on machine learning algorithms, demonstrating novel methods for particle location, tracking, and trajectory separation. The method allows radioactively labeled particles to be located, in three-dimensional space, with high temporal and spatial resolution, requiring no prior knowledge of the number of tracers within the system and can successfully distinguish multiple particles separated by distances as small as 2 mm. The technique's spatial resolution is observed to be invariant with the number of tracers used, allowing large numbers of particles to be tracked simultaneously, with no loss of data quality.
我们介绍了一种基于机器学习算法的正电子发射粒子跟踪新方法，展示了用于粒子定位、跟踪和轨迹分离的新方法。该方法能够在三维空间中以高时间和空间分辨率定位放射性标记的粒子，无需事先了解系统内示踪剂的数量，并且能够成功区分距离小至2毫米的多个粒子。观察到该技术的空间分辨率不随所用示踪剂的数量而变化，从而能够同时跟踪大量粒子，而不会损失数据质量。
