A method for volumetric imaging in radiotherapy using single x-ray projection.
A method for volumetric imaging in radiotherapy using single x-ray projection.
复制标题
一种使用单 X 射线投影的放射治疗体积成像方法。
DOI：
10.1118/1.4918577
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发表时间：
2015
期刊：
影响因子：
3.8
通讯作者：
Jia,Xun
中科院分区：
文献类型：
--
作者：
Xu,Yuan;Yan,Hao;Ouyang,Luo;Wang,Jing;Zhou,Linghong;Cervino,Laura;Jiang,SteveB;Jia,Xun
目的基于相应的x射线投影生成瞬时体图像是一个有趣的问题。本研究的目的是开发一种新的方法，通过稀疏学习方法来实现这一目标。方法将投影图像分割成小矩形块，提取隐藏在投影图像中的运动信息。作者利用稀疏学习方法自动选择与肺运动模型主成分分析（PCA）系数高度相关的斑块。在斑块选择过程中，建立了将斑块强度映射到主成分分析系数的模型。基于该模型，测量投影可用于预测PCA系数，然后进一步用于生成运动矢量场，从而生成体积图像。作者还提出了一种基于分割投影的强度基线校正方法，该方法通过线性变换将模拟投影图像中patch处像素强度的第一矩和第二矩与测量图像中的像素强度进行匹配。该方法在仿真数据和真实数据中都得到了验证。结果该算法能够识别出包含相关运动信息的斑块，如横膈膜区域。在运动预测中，强度基线校正步骤对于消除系统误差非常重要。在仿真案例中，稀疏学习模型将第一个PCA系数的预测误差从未使用稀疏学习时的10%降低到5%，预测运动向量的第95百分位误差从2.40 mm降低到0.92 mm。在肿瘤运动规律的幻影病例中，预测的肿瘤轨迹重建成功，肿瘤中心定位误差为0.82 mm，而未使用稀疏学习方法的肿瘤中心定位误差为1.66 mm。当肿瘤运动由周期和振幅不规则的患者真实呼吸信号驱动时，平均肿瘤中心误差为0.6 mm。本文还研究了算法在稀疏度、补丁大小、是否存在隔膜以及计算时间等方面的鲁棒性。作者开发了一种新方法，可以自动识别x射线投影中的运动信息，并在此基础上生成体积图像。
PurposeIt is an intriguing problem to generate an instantaneous volumetric image based on the corresponding x‐ray projection. The purpose of this study is to develop a new method to achieve this goal via a sparse learning approach.MethodsTo extract motion information hidden in projection images, the authors partitioned a projection image into small rectangular patches. The authors utilized a sparse learning method to automatically select patches that have a high correlation with principal component analysis (PCA) coefficients of a lung motion model. A model that maps the patch intensity to the PCA coefficients was built along with the patch selection process. Based on this model, a measured projection can be used to predict the PCA coefficients, which are then further used to generate a motion vector field and hence a volumetric image. The authors have also proposed an intensity baseline correction method based on the partitioned projection, in which the first and the second moments of pixel intensities at a patch in a simulated projection image are matched with those in a measured one via a linear transformation. The proposed method has been validated in both simulated data and real phantom data.ResultsThe algorithm is able to identify patches that contain relevant motion information such as the diaphragm region. It is found that an intensity baseline correction step is important to remove the systematic error in the motion prediction. For the simulation case, the sparse learning model reduced the prediction error for the first PCA coefficient to 5%, compared to the 10% error when sparse learning was not used, and the 95th percentile error for the predicted motion vector was reduced from 2.40 to 0.92 mm. In the phantom case with a regular tumor motion, the predicted tumor trajectory was successfully reconstructed with a 0.82 mm error for tumor center localization compared to a 1.66 mm error without using the sparse learning method. When the tumor motion was driven by a real patient breathing signal with irregular periods and amplitudes, the average tumor center error was 0.6 mm. The algorithm robustness with respect to sparsity level, patch size, and presence or absence of diaphragm, as well as computation time, has also been studied.ConclusionsThe authors have developed a new method that automatically identifies motion information from an x‐ray projection, based on which a volumetric image is generated.