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用于步进式调强放射治疗的叶片位置优化

Leaf position optimization for step-and-shoot IMRT.

作者信息

De Gersem W, Claus F, De Wagter C, Van Duyse B, De Neve W

机构信息

Division of Radiotherapy, Ghent University Hospital, Ghent, Belgium.

出版信息

Int J Radiat Oncol Biol Phys. 2001 Dec 1;51(5):1371-88. doi: 10.1016/s0360-3016(01)02607-4.

Abstract

PURPOSE

To describe the theoretical basis, the algorithm, and implementation of a tool that optimizes segment shapes and weights for step-and-shoot intensity-modulated radiation therapy delivered by multileaf collimators.

METHODS AND MATERIALS

The tool, called SOWAT (Segment Outline and Weight Adapting Tool) is applied to a set of segments, segment weights, and corresponding dose distribution, computed by an external dose computation engine. SOWAT evaluates the effects of changing the position of each collimating leaf of each segment on an objective function, as follows. Changing a leaf position causes a change in the segment-specific dose matrix, which is calculated by a fast dose computation algorithm. A weighted sum of all segment-specific dose matrices provides the dose distribution and allows computation of the value of the objective function. Only leaf position changes that comply with the multileaf collimator constraints are evaluated. Leaf position changes that tend to decrease the value of the objective function are retained. After several possible positions have been evaluated for all collimating leaves of all segments, an external dose engine recomputes the dose distribution, based on the adapted leaf positions and weights. The plan is evaluated. If the plan is accepted, a segment sequencer is used to make the prescription files for the treatment machine. Otherwise, the user can restart SOWAT using the new set of segments, segment weights, and corresponding dose distribution. The implementation was illustrated using two example cases. The first example is a T1N0M0 supraglottic cancer case that was distributed as a multicenter planning exercise by investigators from Rotterdam, The Netherlands. The exercise involved a two-phase plan. Phase 1 involved the delivery of 46 Gy to a concave-shaped planning target volume (PTV) consisting of the primary tumor volume and the elective lymph nodal regions II-IV on both sides of the neck. Phase 2 involved a boost of 24 Gy to the primary tumor region only. SOWAT was applied to the Phase 1 plan. Parotid sparing was a planning goal. The second implementation example is an ethmoid sinus cancer case, planned with the intent of bilateral visus sparing. The median PTV prescription dose was 70 Gy with a maximum dose constraint to the optic pathway structures of 60 Gy.

RESULTS

The initial set of segments, segment weights, and corresponding dose distribution were obtained, respectively, by an anatomy-based segmentation tool, a segment weight optimization tool, and a differential scatter-air ratio dose computation algorithm as external dose engine. For the supraglottic case, this resulted in a plan that proved to be comparable to the plans obtained at the other institutes by forward or inverse planning techniques. After using SOWAT, the minimum PTV dose and PTV dose homogeneity increased; the maximum dose to the spinal cord decreased from 38 Gy to 32 Gy. The left parotid mean dose decreased from 22 Gy to 19 Gy and the right parotid mean dose from 20 to 18 Gy. For the ethmoid sinus case, the target homogeneity increased by leaf position optimization, together with a better sparing of the optical tracts.

CONCLUSIONS

By using SOWAT, the plans improved with respect to all plan evaluation end points. Compliance with the multileaf collimator constraints is guaranteed. The treatment delivery time remains almost unchanged, because no additional segments are created.

摘要

目的

描述一种用于优化多叶准直器实施的步进式调强放射治疗中射野形状和权重的工具的理论基础、算法及实现方法。

方法和材料

该工具名为SOWAT(射野轮廓与权重适配工具),应用于由外部剂量计算引擎计算得出的一组射野、射野权重及相应的剂量分布。SOWAT评估改变每个射野的每个准直器叶片位置对目标函数的影响,具体如下。改变叶片位置会导致特定射野剂量矩阵发生变化,该矩阵由快速剂量计算算法算出。所有特定射野剂量矩阵的加权和给出剂量分布,并可计算目标函数的值。仅评估符合多叶准直器约束条件的叶片位置变化。倾向于降低目标函数值的叶片位置变化被保留。在对所有射野的所有准直器叶片评估了多个可能位置后,外部剂量引擎根据调整后的叶片位置和权重重新计算剂量分布。对计划进行评估。如果计划被接受,则使用射野排序器生成治疗机的处方文件。否则,用户可使用新的射野集、射野权重及相应的剂量分布重新启动SOWAT。通过两个示例病例展示了该实现方法。第一个示例是一例T1N0M0声门上癌病例,由荷兰鹿特丹的研究人员作为多中心计划演练病例进行分发。该演练涉及一个两阶段计划。第一阶段是向一个凹形计划靶区(PTV)给予46 Gy剂量,该靶区由原发肿瘤体积及双侧颈部II - IV区的选择性淋巴结区域组成。第二阶段仅对原发肿瘤区域给予24 Gy的追加剂量。SOWAT应用于第一阶段计划。腮腺保护是一个计划目标。第二个实现示例是一例筛窦癌病例,计划旨在双侧保留视力。PTV的中位处方剂量为70 Gy,对视路结构的最大剂量约束为60 Gy。

结果

初始的射野集、射野权重及相应的剂量分布分别通过基于解剖的分割工具、射野权重优化工具以及作为外部剂量引擎的微分散射 - 空气比剂量计算算法获得。对于声门上癌病例,这产生了一个与其他机构通过正向或逆向计划技术获得的计划相当的计划。使用SOWAT后,PTV的最小剂量和PTV剂量均匀性增加;脊髓的最大剂量从38 Gy降至32 Gy。左侧腮腺平均剂量从

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