基于深度学习的X射线定位提升混合像素探测器的微米级空间分辨率与可用光子通量
Enhanced micrometre spatial resolution and usable photon flux for hybrid pixel detectors via deep-learning-based X-ray localization
- Paul Scherrer Institute（保罗谢勒研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究针对MÖNCH混合像素探测器，采用深度学习模型定位X射线单光子与堆积事件，提升了空间分辨率与可用光子通量，在刃边测量中实现了更优的分辨率与更高的重建效率。
AI中文摘要：
MÖNCH探测器是一款电荷积分型混合像素探测器，像素间距为25μm，通过利用相邻像素间的电荷共享可实现X射线的微米级空间分辨率。然而，传统的分析插值方法依赖于从电荷共享统计得出的全局映射，仅适用于孤立的单光子簇，这限制了可实现的分辨率和可用光子通量。为克服这些局限，我们基于高保真模拟数据训练深度学习模型，以定位单光子和堆积事件。在12keV的刃边测量中，当低占空比为0.74%（每像素每帧光子数）且单光子事件占主导时，所提方法的边缘扩展函数分辨率达到1.36μm，而传统η插值法为1.54μm。我们实验证明，在2.4倍更高的1.81%占空比下，解析每个簇中多达3个光子可将重建效率从59%提升至93%，同时保持1.39μm的分辨率。
英文摘要：
Micrometre spatial resolution is achieved for X-rays with the MÖNCH detector, a charge-integrating hybrid pixel detector with a 25 μm pixel pitch, by exploiting charge sharing between neighbouring pixels. The conventional analytical interpolation method, however, relies on a global mapping derived from the charge-sharing statistics and is applicable only to isolated single-photon clusters, which limits both the achievable resolution and the usable photon flux. To overcome these limitations, we trained deep learning models to localize single-photon and pile-up events based on high-fidelity simulation data. For knife-edge measurements at 12 keV and a low occupancy of 0.74% (photons per pixel and frame), where single-photon events dominate, the proposed method reaches a of the edge spread function of 1.36 μm, compared with 1.54 μm for the conventional eta interpolation. We demonstrate experimentally that resolving up to three photons per cluster raises the reconstruction efficiency from 59% to 93% at a 2.4{\times} higher occupancy of 1.81%, while preserving a of 1.39 μm.
