A feasibility study of treatment verification using EPID cine images for hypofractionated lung radiotherapy
A feasibility study of treatment verification using EPID cine images for hypofractionated lung radiotherapy
复制标题
使用 EPID 电影图像进行大分割肺部放射治疗的治疗验证的可行性研究
DOI：
10.1088/0031-9155/54/18/s01
复制
发表时间：
2009-09-21
影响因子：
3.5
通讯作者：
Jiang, Steve
中科院分区：
文献类型：
--
作者：
Tang, Xiaoli;Lin, Tong;Jiang, Steve
我们提出了一种新的方法，利用电影EPID(电子门户成像设备)图像进行潜在的在线治疗验证，用于基于机器学习算法的低分割肺放疗。低分割放射治疗需要很高的精度。有效地监测靶点以确保肿瘤位于射束孔径内是至关重要的。我们将治疗验证问题建模为一个两类分类问题，并应用人工神经网络(ANN)将治疗过程中获得的电影EPID图像分类为相应的类别-包括光束孔径内或外的肿瘤。神经网络的训练样本是使用数字重建放射照片(DRR)生成的，其中人工添加了肿瘤位置的移位-以模拟具有不同肿瘤位置的电影EPID图像。利用主成分分析(PCA)对训练样本和处理过程中获得的电影EPID图像进行降维。所提出的治疗验证算法在5名低分割肺患者上进行了回顾性测试。平均而言，我们提出的算法达到了98.0%的分类准确率、97.6%的召回率和99.7%的准确率。
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.