Chen Dong, Xie Hongzhi, Gu Lixu, Liu Jing, Tian Liang
College of Computer and Cyber Security, Hebei Normal University, Shijiazhuang, China; Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Data Security, Hebei Normal University, Shijiazhuang, China; Key Laboratory of Augmented Reality, College of Mathematics and Information Science, Hebei Normal University, Shijiazhuang, China.
Department of Cardiothoracic Surgery, Peking Union Medical College Hospital, Beijing, China.
Comput Biol Med. 2020 Aug;123:103913. doi: 10.1016/j.compbiomed.2020.103913. Epub 2020 Jul 19.
Respiration-introduced tumor location uncertainty is a challenge in lung percutaneous interventions, especially for the respiratory motion estimation of the tumor and surrounding vessel structures. In this work, a local motion modeling method is proposed based on whole-chest computed tomography (CT) and CT-fluoroscopy (CTF) scans. A weighted sparse statistical modeling (WSSM) method that can accurately capture location errors for each landmark point is proposed for lung motion prediction. By varying the sparse weight coefficients of the WSSM method, newly input motion information is approximately represented by a sparse linear combination of the respiratory motion repository and employed to serve as prior knowledge for the following registration process. We have also proposed an adaptive motion prior-based registration method to improve the motion prediction accuracy of the motion model in the region of interest (ROI). This registration method adopts a B-spline scheme to interactively weight the relative influence of the prior knowledge, model surface and image intensity information by locally controlling the deformation in the CTF image region. The proposed method has been evaluated on 15 image pairs between the end-expiratory (EE) and end-inspiratory (EI) phases and 31 four-dimensional CT (4DCT) datasets. The results reveal that the proposed WSSM method achieved a better motion prediction performance than other existing lung statistical motion modeling methods, and the motion prior-based registration method can generate more accurate local motion information in the ROI.
呼吸引入的肿瘤位置不确定性是肺部经皮介入治疗中的一项挑战，尤其是对于肿瘤及周围血管结构的呼吸运动估计而言。在这项工作中，基于全胸部计算机断层扫描（CT）和CT透视（CTF）扫描提出了一种局部运动建模方法。针对肺部运动预测，提出了一种加权稀疏统计建模（WSSM）方法，该方法能够准确捕捉每个地标点的位置误差。通过改变WSSM方法的稀疏权重系数，新输入的运动信息由呼吸运动库的稀疏线性组合近似表示，并用作后续配准过程的先验知识。我们还提出了一种基于自适应运动先验的配准方法，以提高感兴趣区域（ROI）中运动模型的运动预测精度。这种配准方法采用B样条方案，通过局部控制CTF图像区域中的变形，交互式地权衡先验知识、模型表面和图像强度信息的相对影响。所提出的方法已在15对呼气末（EE）和吸气末（EI）相位的图像对以及31个四维CT（4DCT）数据集上进行了评估。结果表明，所提出的WSSM方法比其他现有的肺部统计运动建模方法具有更好的运动预测性能，并且基于运动先验的配准方法能够在ROI中生成更准确的局部运动信息。
