Proton radiography imaging was proposed as a promising technique to evaluate internal anatomical changes, to enable pre-treatment patient alignment, and most importantly, to optimize the patient specific CT number to stopping-power ratio conversion. The clinical implementation rate of proton radiography systems is still limited due to their complex bulky design, together with the persistent problem of (in)elastic nuclear interactions and multiple Coulomb scattering (i.e. range mixing). In this work, a compact multi-energy proton radiography system was proposed in combination with an artificial intelligence network architecture (ProtonDSE) to remove the persistent problem of proton scatter in proton radiography. A realistic Monte Carlo model of the ProteusOne accelerator was built at 200 and 220 MeV to isolate the scattered proton signal in 236 proton radiographies of 80 digital anthropomorphic phantoms. ProtonDSE was trained to predict the proton scatter distribution at two beam energies in a 60%/25%/15% scheme for training, testing, and validation. A calibration procedure was proposed to derive the water equivalent thickness image based on the detector dose response relationship at both beam energies. ProtonDSE network performance was evaluated with quantitative metrics that showed an overall mean absolute percentage error below 1.4%0.4% in our test dataset. For one example patient, detector dose to WET conversions were performed based on the total dose (), the primary proton dose (), and the ProtonDSE corrected detector dose (). The determined WET accuracy was compared with respect to the reference WET by idealistic raytracing in a manually delineated region-of-interest inside the brain. The error was determined 4.3%4.1% for 2.2%1.4% for and 2.5%2.0% for

质子放射成像被提议作为一种有前景的技术来评估内部解剖学变化，使治疗前患者对齐，最重要的是，优化患者特定 CT 数到停止功率比的转换。由于其复杂的庞大设计，以及（非）弹性核相互作用和多次库仑散射（即范围混合）的持续问题，质子射线照相系统的临床实施率仍然受到限制。在这项工作中，结合人工智能网络架构（ProtonDSE），提出了一种紧凑的多能量质子射线照相系统，以消除质子射线照相中持续存在的质子散射问题。Proteus 的真实蒙特卡罗模型一个加速器以 200 和 220 MeV 建造，以隔离 80 个数字拟人模型的 236 个质子射线照相中的散射质子信号。ProtonDSE 被训练用于以 60%/25%/15% 的训练、测试和验证方案预测两种光束能量下的质子散射分布。提出了一种校准程序，以根据两个光束能量下的探测器剂量响应关系导出水当量厚度图像。ProtonDSE 网络性能使用定量指标进行评估，在我们的测试数据集中显示总体平均绝对百分比误差低于 1.4%0.4%。对于一个示例患者，基于总剂量 ( )、主要质子剂量 ( ) 和 ProtonDSE 校正的检测器剂量 (）。通过在大脑内手动划定的感兴趣区域中进行理想的光线追踪，将确定的 WET 精度与参考 WET 进行比较。误差确定为 4.3%4.1% 为2.2%1.4% 为2.5%2.0%