METHODS: The presented approach is based on the pixelated, semiconductor detector Timepix. The large number of pixels with small pitch, all individually calibrated for energy deposition, enables detection and visualization of single particle tracks. For discrimination among different ion species, the pattern recognition analysis of the detector signal is used. Fragmentation spectra resulting from a primary carbon ion beam at various depths of tissue-equivalent material were studied to identify different ion species in mixed particle fields. The performance of the method was evaluated quantitatively using reference data from an established technique.
RESULTS: All ion species resulting from carbon ion fragmentation in tissue-equivalent material could be separated. For measurements behind a 158-mm-thick water tank, the relative fractions of H, He, Be, and B ions detected agreed with corresponding reference data within the limits of uncertainty. For the relatively rare lithium ions, the agreement was within 2.3 Δref (uncertainty of reference).
CONCLUSIONS: For designated configurations, the presented ion type identification method enables studies of therapeutic carbon ion beams with a simple, small, and configurable detection setup. The technique is promising to enable online fragmentation studies over a wide range of beam and target parameters in the future.
方法:提出的方法基于像素化,半导体探测器Timepix.具有小间距的大量像素,所有单独校准的能量沉积,实现单粒子轨迹的检测和可视化。为了区分不同的离子种类,使用检测器信号的模式识别分析。研究了在组织等效材料的不同深度处由一次碳离子束产生的碎片光谱,以识别混合粒子场中的不同离子种类。使用来自既定技术的参考数据定量评估该方法的性能。
结果:可以分离组织等效材料中碳离子碎裂产生的所有离子种类。对于158毫米厚的水箱后面的测量,H的相对分数,他,Be,在不确定度范围内,检测到的B离子与相应的参考数据一致。对于相对稀有的锂离子,协议在2.3Δref(参考不确定度)内。
结论:对于指定的配置,提出的离子类型识别方法使治疗性碳离子束的研究具有简单的,小,和可配置的检测设置。该技术有望在将来实现在宽范围的光束和目标参数上的在线碎裂研究。