Robust optimization for intensity-modulated proton therapy with soft spot sensitivity regularization
Robust optimization for intensity-modulated proton therapy with soft spot sensitivity regularization
复制标题
DOI：
10.1002/mp.13344
复制
发表时间：
2019-03-01
期刊：
影响因子：
3.8
通讯作者：
Sheng, Ke
中科院分区：
文献类型：
--
作者：
Gu, Wenbo;Ruan, Dan;Sheng, Ke
目的
质子剂量分布对射程估算和患者定位的不确定性敏感。目前，质子的稳健性是通过最坏情况场景优化方法来管理的，这种方法计算效率低下。为了克服这些挑战，我们开发了一种新的调强质子治疗（IMPT）优化方法，该方法将剂量保真度与一个灵敏度项相结合，该灵敏度项描述了由于射程和定位不确定性导致的剂量扰动。
方法
在综合优化框架中，优化代价函数被表述为包含两项：一个剂量保真度项和一个稳健性项，稳健性项对扫描点灵敏度和强度的内积进行惩罚。IMPT扫描点对扰动的灵敏度定义为由射程和定位误差引起的剂量分布变化。为了评估灵敏度，首先计算特定点的剂量分布的空间梯度。然后通过所有受影响体素的方向梯度的总绝对值来确定点灵敏度。使用快速迭代收缩阈值算法来解决优化问题。该方法在3例颅底肿瘤（SBT）患者和3例双侧头颈部（H&N）患者上进行了测试。所提出的灵敏度正则化方法（SenR）在临床靶区体积（CTV）和计划靶区体积（PTV）上都得到了应用。它们与传统的基于PTV的优化方法（Conv）和基于CTV的体素级最坏情况场景优化方法（WC）进行了比较。
结果
在没有不确定性的标称条件下，三种方法实现了相似的CTV剂量覆盖，而基于CTV的SenR方法与WC方法相比，更好地保护了危及器官（OARs），对于SBT病例，[平均剂量（Dmean），最大剂量（Dmax）]平均降低了[4.72，3.38]GyRBE，对于H&N病例，平均降低了[2.54，3.33]GyRBE。基于PTV的SenR方法对OAR的保护与WC方法相当。WC方法和SenR方法都比传统的基于PTV的方法提高了计划的稳健性。平均而言，在射程不确定的情况下，CTV的最低[D95%，V95%，V100%]从Conv方法中的[93.75%，88.47%，47.37%]分别提高到WC方法中的[99.28%，99.51%，86.64%]、SenR - CTV方法中的[97.71%，97.85%，81.65%]和SenR - PTV方法中的[98.77%，99.30%，85.12%]。在摆位不确定的情况下，CTV的平均最低[D95%，V95%，V100%]从Conv方法中的[95.35%，94.92%，65.12%]分别提高到WC方法中的[99.43%，99.63%，87.12%]、SenR - CTV方法中的[96.97%，97.13%，77.86%]和SenR - PTV方法中的[98.21%，98.34%，83.88%]。SenR优化的运行时间比体素级最坏情况方法短8倍。
结论
我们为IMPT开发了一种新的计算高效的稳健优化方法。稳健性是通过点对射程和移位扰动的灵敏度来计算的。然后通过灵敏度项对剂量保真度项进行正则化，以在剂量测定和稳健性之间实现灵活性和权衡。在压力测试中，SenR对意外的不确定性更具弹性。这些优势以及其快速的计算时间使其成为临床IMPT计划的一个可行选择。
Purpose Proton dose distribution is sensitive to uncertainties in range estimation and patient positioning. Currently, the proton robustness is managed by worst-case scenario optimization methods, which are computationally inefficient. To overcome these challenges, we develop a novel intensity-modulated proton therapy (IMPT) optimization method that integrates dose fidelity with a sensitivity term that describes dose perturbation as the result of range and positioning uncertainties. Methods In the integrated optimization framework, the optimization cost function is formulated to include two terms: a dose fidelity term and a robustness term penalizing the inner product of the scanning spot sensitivity and intensity. The sensitivity of an IMPT scanning spot to perturbations is defined as the dose distribution variation induced by range and positioning errors. To evaluate the sensitivity, the spatial gradient of the dose distribution of a specific spot is first calculated. The spot sensitivity is then determined by the total absolute value of the directional gradients of all affected voxels. The fast iterative shrinkage-thresholding algorithm is used to solve the optimization problem. This method was tested on three skull base tumor (SBT) patients and three bilateral head-and-neck (H&N) patients. The proposed sensitivity-regularized method (SenR) was implemented on both clinic target volume (CTV) and planning target volume (PTV). They were compared with conventional PTV-based optimization method (Conv) and CTV-based voxel-wise worst-case scenario optimization approach (WC). Results Under the nominal condition without uncertainties, the three methods achieved similar CTV dose coverage, while the CTV-based SenR approach better spared organs at risks (OARs) compared with the WC approach, with an average reduction of [Dmean, Dmax] of [4.72, 3.38] GyRBE for the SBT cases and [2.54, 3.33] GyRBE for the H&N cases. The OAR sparing of the PTV-based SenR method was comparable with the WC method. The WC method, and SenR approaches all improved the plan robustness from the conventional PTV-based method. On average, under range uncertainties, the lowest [D95%, V95%, V100%] of CTV were increased from [93.75%, 88.47%, 47.37%] in the Conv method, to [99.28%, 99.51%, 86.64%] in the WC method, [97.71%, 97.85%, 81.65%] in the SenR-CTV method and [98.77%, 99.30%, 85.12%] in the SenR-PTV method, respectively. Under setup uncertainties, the average lowest [D95%, V95%, V100%] of CTV were increased from [95.35%, 94.92%, 65.12%] in the Conv method, to [99.43%, 99.63%, 87.12%] in the WC method, [96.97%, 97.13%, 77.86%] in the SenR-CTV method, and [98.21%, 98.34%, 83.88%] in the SenR-PTV method, respectively. The runtime of the SenR optimization is eight times shorter than that of the voxel-wise worst-case method. Conclusion We developed a novel computationally efficient robust optimization method for IMPT. The robustness is calculated as the spot sensitivity to both range and shift perturbations. The dose fidelity term is then regularized by the sensitivity term for the flexibility and trade-off between the dosimetry and the robustness. In the stress test, SenR is more resilient to unexpected uncertainties. These advantages in combination with its fast computation time make it a viable candidate for clinical IMPT planning.