He Tiancheng, Xue Zhong, Xie Weixin, Wong Stephen T C
The Center for Bioengineering and Informatics, The Methodist Hospital Research Institute, Department of Radiology, The Methodist Hospital, Weil Cornell Medical College, Houston, TX, USA.
Med Image Comput Comput Assist Interv. 2010;13(Pt 3):392-9. doi: 10.1007/978-3-642-15711-0_49.
In image-guided lung intervention, the electromagnetic (EM) tracked needle can be visualized in a pre-procedural CT by registering the EM tracking and the CT coordinate systems. However, there exist discrepancies between the static pre-procedural CT and the patient due to respiratory motion. This paper proposes an online 4-D CT estimation approach to patient-specific respiratory motion compensation. First, the motion patterns between 4-D CT data and respiratory signals such as fiducials from a number of patients are trained in a template space after image registration. These motion patterns can be used to estimate the patient-specific serial CTs from a static 3-D CT and the real-time respiratory signals of that patient, who do not generally take 4-D CTs. Specifically, the respiratory lung field motion vectors are projected onto the Kernel Principal Component Analysis (K-PCA) space, and a motion estimation model is constructed to estimate the lung field motion from the fiducial motion using the ridge regression method based on the least squares support vector machine (LS-SVM). The algorithm can be performed onsite prior to the intervention to generate the serial CT images according to the respiratory signals in advance, and the estimated CTs can be visualized in real-time during the intervention. In experiments, we evaluated the algorithm using leave-one-out strategy on 30 4-D CT data, and the results showed that the average errors of the lung field surfaces are 1.63 mm.
在图像引导的肺部介入手术中，通过配准电磁（EM）跟踪和CT坐标系，可在术前CT中可视化EM跟踪的针。然而，由于呼吸运动，术前静态CT与患者实际情况之间存在差异。本文提出一种针对特定患者的在线4D CT估计方法，用于呼吸运动补偿。首先，在图像配准后，在模板空间中训练来自多个患者的4D CT数据与呼吸信号（如基准点）之间的运动模式。这些运动模式可用于从静态3D CT和该患者的实时呼吸信号估计特定患者的系列CT，该患者通常未进行4D CT扫描。具体而言，将呼吸肺野运动向量投影到核主成分分析（K-PCA）空间，并基于最小二乘支持向量机（LS-SVM）使用岭回归方法构建运动估计模型，以根据基准点运动估计肺野运动。该算法可在介入手术前在现场执行，以预先根据呼吸信号生成系列CT图像，并且估计的CT可在介入手术期间实时可视化。在实验中，我们使用留一法策略对30组4D CT数据评估了该算法，结果表明肺野表面的平均误差为1.63毫米。
