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GENERATING AND APPLYING ROBUST DOSE PREDICTION MODELS

机译:生成和应用鲁棒剂量预测模型

摘要

Nominal values of parameters, and perturbations of the nominal values, that are associated with previously defined radiation treatment plans are accessed. For each treatment field of the treatment plans, a field-specific planning target volume (fsPTV) is determined based on those perturbations. At least one clinical target volume (CTV) and at least one organ-at-risk (OAR) volume are also delineated. Each OAR includes at least one sub-volume that is delineated based on spatial relationships between each OAR and the CTV and the fsPTV for each treatment field. Dose distributions for the sub-volumes are determined based on the nominal values and the perturbations. One or more dose prediction models are generated for each sub-volume. The dose prediction model(s) are trained using the dose distributions.
机译:访问与先前定义的放射治疗计划相关联的参数的标称参数和扰动的标称值。 对于治疗计划的每个处理领域,基于这些扰动确定特定于特定的规划目标体积(FSPTV)。 至少一个临床靶体积(CTV)和至少一个器官风险(OAR)体积也被描绘。 每个OAR包括至少一个基于每个桨桨与CTV与每个处理场的FSPTV之间的空间关系划定的子体积。 子卷的剂量分布是基于标称值和扰动确定的。 为每个子体积产生一个或多个剂量预测模型。 剂量预测模型使用剂量分布训练。

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