Deformed dose restoration to account for tumor deformation and position changes for adaptive proton therapy
Deformed dose restoration to account for tumor deformation and position changes for adaptive proton therapy
复制标题
变形剂量恢复以适应自适应质子治疗的肿瘤变形和位置变化
DOI：
10.1002/mp.16149
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发表时间：
2022
期刊：
影响因子：
3.8
通讯作者：
Takao Seishin
中科院分区：
文献类型：
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作者：
Miyazaki Koichi;Fujii Yusuke;Yamada Takahiro;Kanehira Takahiro;Miyamoto Naoki;Matsuura Taeko;Yasuda Koichi;Uchinami Yusuke;Otsuka Manami;Aoyama Hidefumi;Takao Seishin
背景调强质子治疗（IMPT）过程中的在线调整可以最大限度地减少分次间解剖结构变化的影响，但由于工作流程复杂，仍然具有挑战性。用于快速和自动在线IMPT自适应的一种方法是剂量恢复，其恢复更新的解剖结构上的初始剂量分布。然而，这种方法可能会失败的情况下，肿瘤的变形或位置changese.PurposeTo开发一种快速和强大的IMPT在线自适应方法命名为“变形剂量恢复（DDR）”，可以调整分次间肿瘤的变形和位置changes.MethodsThe DDR方法包括两个步骤：（1）计算的变形剂量分布，和（2）变形剂量分布的恢复。首先，在初始临床靶体积（CTV）和新的CTV之间执行可变形图像配准（REMM）以计算向量场。为了确保设置和范围不确定性的稳健性以及恢复变形剂量分布的能力，开发了一种扩展的基于CTV的配准，以保持剂量梯度在CTV之外。通过将矢量场应用于初始剂量分布，获得变形的剂量分布。然后，进行逐体素剂量差优化，以计算恢复更新解剖结构上变形剂量分布的射束参数。优化函数为总剂量差与各射野剂量差之和，以恢复各射野的初始剂量重叠。这种方法只需要目标轮廓，这消除了对危险器官（OAR）轮廓的需要。选择6例重复CT检查肿瘤变形和/或位置改变的临床病例。DDR的可行性进行了评估，通过比较结果与其他三个战略，即不适应（继续初始计划），适应以前的剂量恢复，并充分optimized.ResultsIn所有情况下，继续初始计划在很大程度上扭曲的重复CT和剂量体积直方图（DVH）指标的目标减少由于肿瘤变形或位置的变化。另一方面，DDR将靶的DVH指标提高到与初始剂量分布相同的水平。一些OAR的剂量增加，因为肿瘤生长减少了CTV和OAR之间的相对距离。设置和范围不确定性（3 mm/3.5%）的稳健性评价表明，CTV D95%的DVH带宽与初始计划的偏差为0.4% ± 0.5%（平均值± S.D.）对于DDR。计算时间为8.1 ± 6.4 min。结论开发了一种在线自适应算法，提高了分次间解剖结构变化的治疗质量，并保留了分次内设置和范围不确定性的鲁棒性。这种方法的主要优点是它只需要目标轮廓，节省了OAR轮廓的时间。这里描述的用于肿瘤变形和位置变化的快速且鲁棒的自适应方法可以减少离线自适应的需要并提高治疗效率。
BackgroundOnline adaptation during intensity‐modulated proton therapy (IMPT) can minimize the effect of inter‐fractional anatomical changes, but remains challenging because of the complex workflow. One approach for fast and automated online IMPT adaptation is dose restoration, which restores the initial dose distribution on the updated anatomy. However, this method may fail in cases where tumor deformation or position changes occur.PurposeTo develop a fast and robust IMPT online adaptation method named “deformed dose restoration (DDR)” that can adjust for inter‐fractional tumor deformation and position changes.MethodsThe DDR method comprises two steps: (1) calculation of the deformed dose distribution, and (2) restoration of the deformed dose distribution. First, the deformable image registration (DIR) between the initial clinical target volume (CTV) and the new CTV were performed to calculate the vector field. To ensure robustness for setup and range uncertainty and the ability to restore the deformed dose distribution, an expanded CTV‐based registration to maintain the dose gradient outside the CTV was developed. The deformed dose distribution was obtained by applying the vector field to the initial dose distribution. Then, the voxel‐by‐voxel dose difference optimization was performed to calculate beam parameters that restore the deformed dose distribution on the updated anatomy. The optimization function was the sum of total dose differences and dose differences of each field to restore the initial dose overlap of each field. This method only requires target contouring, which eliminates the need for organs at risk (OARs) contouring. Six clinical cases wherein the tumor deformation and/or position changed on repeated CTs were selected. DDR feasibility was evaluated by comparing the results with those from three other strategies, namely, not adapted (continuing the initial plan), adapted by previous dose restoration, and fully optimized.ResultsIn all cases, continuing the initial plan was largely distorted on the repeated CTs and the dose‐volume histogram (DVH) metrics for the target were reduced due to the tumor deformation or position changes. On the other hand, DDR improved DVH metrics for the target to the same level as the initial dose distribution. Dose increase was seen for some OARs because tumor growth had reduced the relative distance between CTVs and OARs. Robustness evaluation for setup and range uncertainty (3 mm/3.5%) showed that deviation in DVH‐bandwidth for CTV D95%from the initial plan was 0.4% ± 0.5% (Mean ± S.D.) for DDR. The calculation time was 8.1 ± 6.4 min.ConclusionsAn online adaptation algorithm was developed that improved the treatment quality for inter‐fractional anatomical changes and retained robustness for intra‐fractional setup and range uncertainty. The main advantage of this method is that it only requires target contouring alone and saves the time for OARs contouring. The fast and robust adaptation method for tumor deformation and position changes described here can reduce the need for offline adaptation and improve treatment efficiency.