Kieslich Aaron, Schellhammer Sonja M, Zwanenburg Alex, Kögler Toni, Löck Steffen
OncoRay - National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany.
Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiooncology - OncoRay, Dresden, Germany.
Phys Imaging Radiat Oncol. 2025 Jun 2;35:100788. doi: 10.1016/j.phro.2025.100788. eCollection 2025 Jul.
Prompt gamma-ray timing (PGT) and prompt gamma-ray spectroscopy (PGS) are non-invasive techniques for dose delivery monitoring in proton radiotherapy. Integrating PGT and PGS into a unified data analysis framework may improve proton range verification by incorporating both temporal and spectral information from prompt gamma-ray events. This study evaluates the effectiveness of this integration for enhancing the accuracy of proton range verification using machine-learning.
A homogeneous phantom was irradiated with 162 and 225 MeV static and scanned proton beams. Air cavities of 5, 10 and 20 mm were introduced to simulate anatomical variations. The energy and time of arrival of prompt gamma rays were measured using a PGT detector. 2-dimensional time-energy spectra were extracted for 1,440 proton spots. Different feature sets (energy-only, time-only, energy-restricted time, image) were computed. These feature sets were used by four different machine-learning models to predict range shifts. Model performance was assessed using the root mean square error (RMSE).
Time-only and combined time-energy feature sets exhibited good performance with RMSE values of 3 to 4 mm, consistent with previously developed models. Energy-only and image features led to poorer performance with RMSE values exceeding 5 mm. The integration of energy-only features did not improve prediction accuracy compared to exclusively using time-only features.
While spectral information did not contribute additional value for determining proton beam range shifts in the investigated setup, the findings show that temporal information alone is sufficient to perform accurate proton range verification.
瞬发伽马射线计时（PGT）和瞬发伽马射线光谱学（PGS）是质子放射治疗中用于剂量输送监测的非侵入性技术。将PGT和PGS整合到一个统一的数据分析框架中，通过纳入瞬发伽马射线事件的时间和光谱信息，可能会改善质子射程验证。本研究评估了这种整合对于使用机器学习提高质子射程验证准确性的有效性。
用162和225 MeV的静态扫描质子束照射均匀体模。引入5、10和20 mm的气腔以模拟解剖学变异。使用PGT探测器测量瞬发伽马射线的能量和到达时间。提取了1440个质子点的二维时间-能量谱。计算了不同的特征集（仅能量、仅时间、能量限制时间、图像）。这些特征集被四个不同的机器学习模型用于预测射程偏移。使用均方根误差（RMSE）评估模型性能。
仅时间和组合的时间-能量特征集表现出良好的性能，RMSE值为3至4 mm，与先前开发的模型一致。仅能量和图像特征导致性能较差，RMSE值超过5 mm。与仅使用时间特征相比，仅能量特征的整合并未提高预测准确性。
虽然在研究的设置中，光谱信息对于确定质子束射程偏移没有额外价值，但研究结果表明仅时间信息就足以进行准确的质子射程验证。
