Tang Xiaoli, Lin Tong, Jiang Steve
Department of Radiation Oncology, University of California San Diego, La Jolla, CA 92093, USA.
Phys Med Biol. 2009 Sep 21;54(18):S1-8. doi: 10.1088/0031-9155/54/18/S01. Epub 2009 Aug 18.
We propose a novel approach for potential online 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 the 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.
我们提出了一种基于机器学习算法的全新方法，用于利用电影式电子射野影像装置（EPID）图像对低分割肺部放疗进行潜在的在线治疗验证。低分割放疗需要高精度。有效监测靶区以确保肿瘤位于射野范围内至关重要。我们将治疗验证问题建模为二类分类问题，并应用人工神经网络（ANN）将治疗期间获取的电影式EPID图像分类到相应类别——肿瘤在射野内或射野外。使用数字重建射线影像（DRR）为人工神经网络生成训练样本，并在肿瘤位置人为添加偏移，以模拟具有不同肿瘤位置的电影式EPID图像。主成分分析（PCA）用于降低训练样本以及治疗期间获取的电影式EPID图像的维度。所提出的治疗验证算法以回顾性方式在五例低分割肺部放疗患者身上进行了测试。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%的精确率。
