Positron emission particle tracking using machine learning.
Positron emission particle tracking using machine learning.
复制标题
使用机器学习进行正电子发射粒子跟踪。
DOI：
10.1063/1.5129251
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
C. Windows
中科院分区：
文献类型：
--
作者：
A. Nicuşan;C. Windows
我们介绍了一种新的方法，正电子发射粒子跟踪的基础上机器学习算法，展示粒子定位，跟踪和轨迹分离的新方法。该方法允许在三维空间中以高的时间和空间分辨率定位放射性标记的颗粒，不需要系统内示踪剂数量的先验知识，并且可以成功区分间隔小至2 mm的距离的多个颗粒。观察到该技术的空间分辨率与所使用的示踪剂数量无关，允许同时跟踪大量粒子而不损失数据质量。
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.
DOI：
10.1016/j.nima.2012.04.037
发表时间：
2012-08-01
影响因子：
1.4
作者：
Bickell, M.;Buffler, A.;Parker, D. J.
通讯作者：
Parker, D. J.