使用EPID电影图像进行大分割肺放疗治疗验证的可行性研究
A feasibility study of treatment verification using EPID cine images for hypofractionated lung radiotherapy
摘要：
我们提出了一种新的方法，潜在的在线治疗验证使用电影EPID（电子射野成像设备）图像的大分割肺放疗的基础上，机器学习算法。大分割放射治疗需要高精度。必须有效地监测目标，以确保肿瘤在光束孔径内。我们将治疗验证问题建模为两类分类问题，并应用人工神经网络（ANN）将治疗期间采集的电影EPID图像分类为相应的类别，其中肿瘤位于光束孔径内部或外部。使用人工添加肿瘤位置偏移的数字重建放射照片（DRR）生成ANN的训练样本，以模拟具有不同肿瘤位置的电影EPID图像。主成分分析（PCA）被用来减少训练样本和电影EPID图像在治疗过程中获得的维数。以回顾性方式对5名低分次肺患者进行了拟议的治疗验证算法测试。平均而言，我们提出的算法实现了98.0%的分类准确率，97.6%的召回率和99.7%的准确率。
英文摘要：
We propose a novel approach for potential on-line treatment verification using cine EPID (Electronic Portal Imaging Device) images for hypofractionated lung radiotherapy based on a machine learning algorithm. Hypofractionated radiotherapy requires high precision. It is essential to effectively monitor the target to ensure that the tumor is within the beam aperture. We modeled the treatment verification problem as a two-class classification problem and applied an Artificial Neural Network (ANN) to classify the cine EPID images acquired during the treatment into corresponding classes with the tumor inside or outside of the beam aperture. Training samples were generated for the ANN using digitally reconstructed radiographs (DRRs) with artificially added shifts in tumor location to simulate cine EPID images with different tumor locations. Principal Component Analysis (PCA) was used to reduce the dimensionality of the training samples and cine EPID images acquired during the treatment. The proposed treatment verification algorithm was tested on five hypofractionated lung patients in a retrospective fashion. On average, our proposed algorithm achieved a 98.0% classification accuracy, a 97.6% recall rate, and a 99.7% precision rate.
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