Faculty of Physics, Babeș-Bolyai University, Cluj-Napoca, Romania.
Department of Radiotherapy, The Oncology Institute 'Prof. Dr Ion Chiricuta', Cluj-Napoca, Romania.
Phys Med Biol. 2024 Jul 30;69(16). doi: 10.1088/1361-6560/ad61b8.
This study aims to assess the composition of scattered particles generated in proton therapy for tumors situated proximal to some titanium (Ti) dental implants. The investigation involves decomposing the mixed field and recording Linear Energy Transfer (LET) spectra to quantify the influence of metallic dental inserts located behind the tumor.A therapeutic conformal proton beam was used to deliver the treatment plan to an anthropomorphic head phantom with two types of implants inserted in the target volume (made of Ti and plastic, respectively). The scattered radiation resulted during the irradiation was detected by a hybrid semiconductor pixel detector MiniPIX Timepix3 that was placed distal to the Spread-out Bragg peak. Visualization and field decomposition of stray radiation were generated using algorithms trained in particle recognition based on artificial intelligence neural networks (AI NN). Spectral sensitive aspects of the scattered radiation were collected using two angular positions of the detector relative to the beam direction: 0° and 60°.Using AI NN, 3 classes of particles were identified: protons, electrons & photons, and ions & fast neutrons. Placing a Ti implant in the beam's path resulted in predominantly electrons and photons, contributing 52.2% of the total number of detected particles, whereas for plastic implants, the contribution was 65.4%. Scattered protons comprised 45.5% and 31.9% with and without metal inserts, respectively. The LET spectra were derived for each group of particles identified, with values ranging from 0.01 to 7.5 keVmfor Ti implants/plastic implants. The low-LET component was primarily composed of electrons and photons, while the high-LET component corresponded to protons and ions.This method, complemented by directional maps, holds the potential for evaluating and validating treatment plans involving stray radiation near organs at risk, offering precise discrimination of the mixed field, and enhancing in this way the LET calculation.
本研究旨在评估质子治疗靠近钛（Ti）牙科植入物的肿瘤时产生的散射粒子的组成。该研究涉及分解混合场并记录线性能量传递（LET）谱，以量化位于肿瘤后面的金属牙科插入物的影响。使用治疗适形质子束将治疗计划递送到目标体积中插入两种类型植入物的人体头部模型（分别由 Ti 和塑料制成）。在照射过程中产生的散射辐射由放置在扩展布拉格峰远端的混合半导体像素探测器 MiniPIX Timepix3 检测。使用基于人工智能神经网络（AI NN）的粒子识别训练的算法生成杂散辐射的可视化和场分解。使用探测器相对于光束方向的两个角度位置收集散射辐射的谱敏感方面：0°和 60°。使用 AI NN，识别出 3 类粒子：质子、电子和光子，以及离子和快中子。在光束路径中放置 Ti 植入物会导致主要是电子和光子，占检测到的粒子总数的 52.2%，而对于塑料植入物，贡献为 65.4%。有和没有金属植入物时，散射质子分别占 45.5%和 31.9%。为每个识别出的粒子组导出 LET 谱，其值范围为 0.01 至 7.5 keV，Ti 植入物/塑料植入物。低 LET 成分主要由电子和光子组成，而高 LET 成分对应于质子和离子。这种方法辅以方向图，具有评估和验证涉及风险器官附近杂散辐射的治疗计划的潜力，能够精确区分混合场，并通过这种方式增强 LET 计算。
