PDF(Pubmed)
Abstract：
Radiation therapy relies on quality assurance (QA) to verify dose delivery accuracy. However, current QA methods suffer from operation lag as well as inaccurate performance. Hence, to address these shortcomings, this paper proposes a QA neural network model based on branch architecture, which is based on the analysis of the category features of the QA complexity metrics. The designed branch network focuses on category features, which effectively improves the feature extraction capability for complexity metrics. The branch features extracted by the model are fused to predict the GPR for more accurate QA. The performance of the proposed method was validated on the collected dataset. The experiments show that the prediction performance of the model outperforms other QA methods; the average prediction errors for the test set are 2.12% (2%/2 mm), 1.69% (3%/2 mm), and 1.30% (3%/3 mm). Moreover, the results indicate that two-thirds of the validation samples\' model predictions perform better than the clinical evaluation results, suggesting that the proposed model can assist physicists in the clinic.
摘要：
放射治疗依赖于质量保证(QA)来验证剂量递送准确性。然而,当前的QA方法存在操作滞后和不准确的性能。因此，为了解决这些缺点，提出了一种基于分支结构的QA神经网络模型,这是基于对QA复杂性度量的类别特征的分析。设计的分支网络侧重于类别特征，有效提高了对复杂度度量的特征提取能力。通过模型提取的分支特征被融合以预测GPR以获得更准确的QA。在收集的数据集上验证了所提出方法的性能。实验表明，该模型的预测性能优于其他QA方法；测试集的平均预测误差为2.12%(2%/2mm)，1.69%（3%/2毫米），和1.30%（3%/3毫米）。此外，结果表明，三分之二的验证样本模型预测的表现优于临床评估结果，这表明所提出的模型可以帮助临床物理学家。
