Abstract：
BACKGROUND: Real-time dose estimation is a key-prerequisite to enable online intra-fraction treatment adaptation in magnetic resonance (MR)-guided radiotherapy (MRgRT). It is an essential component for the assessment of the dosimetric benefits and risks of online adaptive treatments, such as multi-leaf collimator (MLC)-tracking.
OBJECTIVE: We present a proof-of-concept for a software workflow for real-time dose estimation of MR-guided adaptive radiotherapy based on real-time data-streams of the linac delivery parameters and target positions.
METHODS: A software workflow, combining our in-house motion management software DynaTrack, a real-time dose calculation engine that connects to a research version of the treatment planning software (TPS) Monaco (v.6.09.00, Elekta AB, Stockholm, Sweden) was developed and evaluated. MR-guided treatment delivery on the Elekta Unity MR-linac was simulated with and without MLC-tracking for three prostate patients, previously treated on the Elekta Unity MR-linac (36.25 Gy/five fractions). Three motion scenarios were used: no motion, regular motion, and erratic prostate motion. Accumulated monitor units (MUs), centre of mass target position and MLC-leaf positions, were forwarded from DynaTrack at a rate of 25 Hz to a Monte Carlo (MC) based dose calculation engine which utilises the research GPUMCD-library (Elekta AB, Stockholm, Sweden). A rigid isocentre shift derived from the selected motion scenarios was applied to a bulk density-assigned session MR-image. The respective electron density used for treatment planning was accessed through the research Monaco TPS. The software workflow including the online dose reconstruction was validated against offline dose reconstructions. Our investigation showed that MC-based real-time dose calculations that account for all linac states (including MUs, MLC positions and target position) were infeasible, hence states were randomly sampled and used for calculation as follows; Once a new linac state was received, a dose calculation with 106 photons was started. Linac states that arrived during the time of the ongoing calculation were put into a queue. After completion of the ongoing calculation, one new linac state was randomly picked from the queue and assigned the MU accumulated from the previous state until the last sample in the queue. The queue was emptied, and the process repeated throughout treatment simulation.
RESULTS: On average 27% (23%-30%) of received samples were used in the real-time calculation, corresponding to a calculation time for one linac state of 148 ms. Median gamma pass rate (2%/3 mm local) was 100.0% (99.9%-100%) within the PTV volume and 99.1% (90.1%-99.4.0%) with a 15% dose cut off. Differences in PTVDmean , CTVDmean , RectumD2% , and BladderD2% (offline-online, % of prescribed dose) were below 0.64%. Beam-by-beam comparisons showed deviations below 0.07 Gy. Repeated simulations resulted in standard deviations below 0.31% and 0.12 Gy for the investigated volume and dose criteria respectively.
CONCLUSIONS: Real-time dose estimation was successfully performed using the developed software workflow for different prostate motion traces with and without MLC-tracking. Negligible dosimetric differences were seen when comparing online and offline reconstructed dose, enabling online intra-fraction treatment decisions based on estimates of the delivered dose.
OBJECTIVE: We present a proof-of-concept for a software workflow for real-time dose estimation of MR-guided adaptive radiotherapy based on real-time data-streams of the linac delivery parameters and target positions.
METHODS: A software workflow, combining our in-house motion management software DynaTrack, a real-time dose calculation engine that connects to a research version of the treatment planning software (TPS) Monaco (v.6.09.00, Elekta AB, Stockholm, Sweden) was developed and evaluated. MR-guided treatment delivery on the Elekta Unity MR-linac was simulated with and without MLC-tracking for three prostate patients, previously treated on the Elekta Unity MR-linac (36.25 Gy/five fractions). Three motion scenarios were used: no motion, regular motion, and erratic prostate motion. Accumulated monitor units (MUs), centre of mass target position and MLC-leaf positions, were forwarded from DynaTrack at a rate of 25 Hz to a Monte Carlo (MC) based dose calculation engine which utilises the research GPUMCD-library (Elekta AB, Stockholm, Sweden). A rigid isocentre shift derived from the selected motion scenarios was applied to a bulk density-assigned session MR-image. The respective electron density used for treatment planning was accessed through the research Monaco TPS. The software workflow including the online dose reconstruction was validated against offline dose reconstructions. Our investigation showed that MC-based real-time dose calculations that account for all linac states (including MUs, MLC positions and target position) were infeasible, hence states were randomly sampled and used for calculation as follows; Once a new linac state was received, a dose calculation with 106 photons was started. Linac states that arrived during the time of the ongoing calculation were put into a queue. After completion of the ongoing calculation, one new linac state was randomly picked from the queue and assigned the MU accumulated from the previous state until the last sample in the queue. The queue was emptied, and the process repeated throughout treatment simulation.
RESULTS: On average 27% (23%-30%) of received samples were used in the real-time calculation, corresponding to a calculation time for one linac state of 148 ms. Median gamma pass rate (2%/3 mm local) was 100.0% (99.9%-100%) within the PTV volume and 99.1% (90.1%-99.4.0%) with a 15% dose cut off. Differences in PTVDmean , CTVDmean , RectumD2% , and BladderD2% (offline-online, % of prescribed dose) were below 0.64%. Beam-by-beam comparisons showed deviations below 0.07 Gy. Repeated simulations resulted in standard deviations below 0.31% and 0.12 Gy for the investigated volume and dose criteria respectively.
CONCLUSIONS: Real-time dose estimation was successfully performed using the developed software workflow for different prostate motion traces with and without MLC-tracking. Negligible dosimetric differences were seen when comparing online and offline reconstructed dose, enabling online intra-fraction treatment decisions based on estimates of the delivered dose.
摘要：
背景：实时剂量估计是在磁共振（MR）引导的放射治疗（MRgRT）中实现在线部分内治疗适应的关键前提。它是评估在线适应性治疗的剂量学益处和风险的重要组成部分，例如多叶准直器(MLC)跟踪。
目的：我们提出了一种基于直线加速器输送参数和目标位置的实时数据流的MR引导自适应放射治疗的实时剂量估计软件工作流程的概念验证。
方法：软件工作流程，结合我们的内部运动管理软件DynaTrack，一个实时剂量计算引擎，连接到一个研究版本的治疗计划软件（TPS）摩纳哥（v.6.09.00，ElektaAB，斯德哥尔摩,瑞典）进行了开发和评估。在ElektaUnityMR-linac上的MR引导治疗交付进行了模拟，并对三名前列腺患者进行了MLC跟踪，先前在ElektaUnityMR-linac上治疗（36.25Gy/五个部分）。使用了三种运动场景:没有运动，有规律的运动,和不稳定的前列腺运动。累计监控单元(MU),质心目标位置和MLC叶片位置，以25Hz的速率从DynaTrack转发到基于蒙特卡洛（MC）的剂量计算引擎，该引擎利用研究GPUMCD库（ElektaAB，斯德哥尔摩,瑞典)。从选定的运动场景得出的刚性等中心偏移被应用于分配了体积密度的会话MR图像。通过摩纳哥TPS研究获得了用于治疗计划的相应电子密度。针对离线剂量重建验证了包括在线剂量重建的软件工作流程。我们的调查表明，基于MC的实时剂量计算考虑了所有直线加速器状态（包括MU，MLC位置和目标位置)不可行，因此，状态被随机采样并用于计算如下；一旦收到新的直线加速器状态，用106个光子开始剂量计算。Linac指出，在正在进行的计算期间到达的时间被放入队列中。正在进行的计算完成后，从队列中随机挑选一个新的直线加速器状态，并分配从上一个状态累积的MU，直到队列中的最后一个样本。队列被清空了,并且在整个处理模拟中重复该过程。
结果：在实时计算中平均使用了27％（23％-30％）的收到样本，对应于一个直线加速器状态的148ms的计算时间。在PTV体积内，中位γ通过率（2％/3mm局部）为100.0％（99.9％-100％），在15％的剂量截止时为99.1％（90.1％-99.4％）。PTVDmean的差异，CTVDmean,RectumD2%,和BladderD2%(离线-在线，处方剂量的%）低于0.64%。逐束比较显示偏差低于0.07Gy。重复模拟导致所研究体积和剂量标准的标准偏差分别低于0.31%和0.12Gy。
结论：使用开发的软件工作流程，在有和没有MLC跟踪的情况下，对不同的前列腺运动轨迹成功地进行了实时剂量估计。当比较在线和离线重建剂量时，可以看到可忽略的剂量学差异，基于所递送剂量的估计,实现在线分次内治疗决策。
目的：我们提出了一种基于直线加速器输送参数和目标位置的实时数据流的MR引导自适应放射治疗的实时剂量估计软件工作流程的概念验证。
方法：软件工作流程，结合我们的内部运动管理软件DynaTrack，一个实时剂量计算引擎，连接到一个研究版本的治疗计划软件（TPS）摩纳哥（v.6.09.00，ElektaAB，斯德哥尔摩,瑞典）进行了开发和评估。在ElektaUnityMR-linac上的MR引导治疗交付进行了模拟，并对三名前列腺患者进行了MLC跟踪，先前在ElektaUnityMR-linac上治疗（36.25Gy/五个部分）。使用了三种运动场景:没有运动，有规律的运动,和不稳定的前列腺运动。累计监控单元(MU),质心目标位置和MLC叶片位置，以25Hz的速率从DynaTrack转发到基于蒙特卡洛（MC）的剂量计算引擎，该引擎利用研究GPUMCD库（ElektaAB，斯德哥尔摩,瑞典)。从选定的运动场景得出的刚性等中心偏移被应用于分配了体积密度的会话MR图像。通过摩纳哥TPS研究获得了用于治疗计划的相应电子密度。针对离线剂量重建验证了包括在线剂量重建的软件工作流程。我们的调查表明，基于MC的实时剂量计算考虑了所有直线加速器状态（包括MU，MLC位置和目标位置)不可行，因此，状态被随机采样并用于计算如下；一旦收到新的直线加速器状态，用106个光子开始剂量计算。Linac指出，在正在进行的计算期间到达的时间被放入队列中。正在进行的计算完成后，从队列中随机挑选一个新的直线加速器状态，并分配从上一个状态累积的MU，直到队列中的最后一个样本。队列被清空了,并且在整个处理模拟中重复该过程。
结果：在实时计算中平均使用了27％（23％-30％）的收到样本，对应于一个直线加速器状态的148ms的计算时间。在PTV体积内，中位γ通过率（2％/3mm局部）为100.0％（99.9％-100％），在15％的剂量截止时为99.1％（90.1％-99.4％）。PTVDmean的差异，CTVDmean,RectumD2%,和BladderD2%(离线-在线，处方剂量的%）低于0.64%。逐束比较显示偏差低于0.07Gy。重复模拟导致所研究体积和剂量标准的标准偏差分别低于0.31%和0.12Gy。
结论：使用开发的软件工作流程，在有和没有MLC跟踪的情况下，对不同的前列腺运动轨迹成功地进行了实时剂量估计。当比较在线和离线重建剂量时，可以看到可忽略的剂量学差异，基于所递送剂量的估计,实现在线分次内治疗决策。
