首页> 外文期刊>Radiotherapy and oncology: Journal of the European Society for Therapeutic Radiology and Oncology >Lung 4D-IMRT treatment planning: an evaluation of three methods applied to four-dimensional data sets.
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Lung 4D-IMRT treatment planning: an evaluation of three methods applied to four-dimensional data sets.

机译:肺部4D-IMRT治疗计划:对应用于多维数据集的三种方法的评估。

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PURPOSE: To compare 4D-dose distributions for IMRT planning on three data sets: a single 4D-CT phase, a 4D-CT phase with a density override to the tumor motion envelope (TME) volume, and the average intensity projection (AIP). METHODS: Eight planning cases were considered. IMRT inverse planning optimization was performed on each of the three data set types, for each case considered. The plans were then applied to all ten phases of the associated 4D-CT data set. The dose to the GTV in each breathing phase was compared to the TME dose from the optimized dose distribution, as well as the GTV dose determined from a model-based deformable registration algorithm. RESULTS: IMRT optimization on a single 3D data set resulted in a greater equivalent uniform dose (EUD) to the GTV when applied to a 4D-CT data set than the EUD for the TME in the optimized plan. The difference was up to 5.5Gy in one case. For all cases and planning techniques considered, a maximum difference of 0.3Gy in the NTDmean to the healthy lung throughout the breathing cycle was found. CONCLUSIONS: For tumors located in the periphery of the lung, optimization on the AIP image resulted in a more uniform GTV dose throughout the breathing cycle. Averages in GTV EUD and healthy lung NTDmean taken over all the breathing phases were found to be in agreement with the dose effect parameters obtained from model-based deformable registration algorithms. All planning methods yielded GTV EUD values that were larger than the prescribed dose when the full 4D data set was considered.
机译:目的:在以下三个数据集上比较用于IMRT计划的4D剂量分布:单个4D-CT阶段,密度覆盖肿瘤运动包膜(TME)体积的4D-CT阶段和平均强度投影(AIP) 。方法:考虑了八个计划案例。针对每种情况,对三种数据集类型均进行了IMRT逆向计划优化。然后将该计划应用于相关4D-CT数据集的所有十个阶段。将每个呼吸阶段中GTV的剂量与优化剂量分布中的TME剂量以及从基于模型的可变形配准算法确定的GTV剂量进行比较。结果:在优化计划中,对3D数据集进行IMRT优化后,将GTV应用于4D-CT数据集所产生的等效均匀剂量(EUD)要比针对TME的EUD大。在一种情况下,差异高达5.5Gy。对于所有考虑的情况和计划技术,在整个呼吸周期中,NTDmean与健康肺部的最大差异为0.3Gy。结论:对于位于肺部周围的肿瘤,在AIP图像上进行优化可以使整个呼吸周期的GTV剂量更均匀。发现在所有呼吸阶段中获取的GTV EUD和健康肺NTDmean的平均值与从基于模型的可变形配准算法获得的剂量效应参数一致。当考虑完整的4D数据集时,所有计划方法产生的GTV EUD值均大于规定剂量。

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