Clinical implementation of full Monte Carlo dose calculation in proton beam therapy
Clinical implementation of full Monte Carlo dose calculation in proton beam therapy
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DOI：
10.1088/0031-9155/53/17/023
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发表时间：
2008-09-07
影响因子：
3.5
通讯作者：
Engelsman, Martijn
中科院分区：
文献类型：
--
作者：
Paganetti, Harald;Jiang, Hongyu;Engelsman, Martijn
这项工作的目标是促进蒙特卡罗质子剂量计算的临床应用，以支持常规治疗计划和实施。使用Monte Carlo代码Geant 4模拟治疗头设置，包括调制器轮（用于宽射束调制）和磁场设置（用于射束扫描）的时间依赖性模拟。可以根据设施的治疗控制系统对任何患者区域特定设置进行建模。该代码以体模测量为基准。在治疗头中使用电离室阅读的模拟允许以绝对单位（戈伊/电离室阅读）指定蒙特卡罗剂量。接下来，将阅读CT数据信息的功能实现到Monte Carlo代码中，以对患者解剖结构进行建模。为了允许时间有效的剂量计算，修改了标准Geant 4跟踪算法。最后，建立了蒙特卡罗剂量引擎与患者数据库和商业计划系统的软件链接，以允许数据交换，从而完成质子蒙特卡罗剂量计算引擎（“DoC ++”）的实施。蒙特卡洛重新计算的计划是一个有价值的工具，重新审视规划过程中的决策。识别蒙特卡罗和基于双射束的剂量计算之间的临床显著差异也可以推动当前双射束方法的改进。作为一个例子，分析了4名头颈部肿瘤患者（共29个视野）。特别是在接近范围末端时，由于剂量退化和由于射束路径中的骨解剖结构引起的范围预测的总体差异，确定了MonteCarlo算法和MonteCarlo之间的差异。我们的实施是针对特定的蒙特卡罗代码和治疗计划系统XIO（计算机化医疗系统公司）。然而，这项工作描述了在临床环境中实施质子蒙特卡罗剂量计算时的一般挑战和考虑因素。所提出的解决方案可以很容易地采用其他规划系统或其他蒙特卡洛代码。
The goal of this work was to facilitate the clinical use of Monte Carlo proton dose calculation to support routine treatment planning and delivery. The Monte Carlo code Geant4 was used to simulate the treatment head setup, including a time-dependent simulation of modulator wheels (for broad beam modulation) and magnetic field settings (for beam scanning). Any patient-field-specific setup can be modeled according to the treatment control system of the facility. The code was benchmarked against phantom measurements. Using a simulation of the ionization chamber reading in the treatment head allows the Monte Carlo dose to be specified in absolute units (Gy per ionization chamber reading). Next, the capability of reading CT data information was implemented into the Monte Carlo code to model patient anatomy. To allow time-efficient dose calculation, the standard Geant4 tracking algorithm was modified. Finally, a software link of the Monte Carlo dose engine to the patient database and the commercial planning system was established to allow data exchange, thus completing the implementation of the proton Monte Carlo dose calculation engine ('DoC++'). Monte Carlo re-calculated plans are a valuable tool to revisit decisions in the planning process. Identification of clinically significant differences between Monte Carlo and pencil-beam-based dose calculations may also drive improvements of current pencil-beam methods. As an example, four patients (29 fields in total) with tumors in the head and neck regions were analyzed. Differences between the pencil-beam algorithm and Monte Carlo were identified in particular near the end of range, both due to dose degradation and overall differences in range prediction due to bony anatomy in the beam path. Further, the Monte Carlo reports dose-to-tissue as compared to dose-to-water by the planning system. Our implementation is tailored to a specific Monte Carlo code and the treatment planning system XiO (Computerized Medical Systems Inc.). However, this work describes the general challenges and considerations when implementing proton Monte Carlo dose calculation in a clinical environment. The presented solutions can be easily adopted for other planning systems or other Monte Carlo codes.