College of Computer and Cyber Security, Hebei Normal University, Shijiazhuang, China.
Key Laboratory of Network and Information Security, Hebei Normal University, Shijiazhuang, China.
Int J Comput Assist Radiol Surg. 2020 Aug;15(8):1279-1290. doi: 10.1007/s11548-020-02154-7. Epub 2020 Apr 28.
Lung biopsy is currently the most effective procedure for cancer diagnosis. However, respiration-induced location uncertainty presents a challenge in precise lung biopsy. To reduce the medical image requirements for motion modelling, in this study, local lung motion information in the region of interest (ROI) is extracted from whole chest computed tomography (CT) and CT-fluoroscopy scans to predict the motion of potentially cancerous tissue and important vessels during the model-driven lung biopsy process.
The motion prior of the ROI was generated via a sparse linear combination of a subset of motion information from a respiratory motion repository, and a weighted sparse-based statistical model was used to preserve the local respiratory motion details. We also employed a motion prior-based registration method to improve the motion estimation accuracy in the ROI and designed adaptive variable coefficients to interactively weigh the relative influence of the prior knowledge and image intensity information during the registration process.
The proposed method was applied to ten test subjects for the estimation of the respiratory motion field. The quantitative analysis resulted in a mean target registration error of 1.5 (0.8) mm and an average symmetric surface distance of 1.4 (0.6) mm.
The proposed method shows remarkable advantages over traditional methods in preserving local motion details and reducing the estimation error in the ROI. These results also provide a benchmark for lung respiratory motion modelling in the literature.
肺活检是目前癌症诊断最有效的方法。然而，呼吸引起的位置不确定性给精确的肺活检带来了挑战。为了减少运动建模对医学图像的要求，本研究从全胸部 CT 和 CT 透视扫描中提取感兴趣区域（ROI）的局部肺运动信息，以预测在模型驱动的肺活检过程中潜在癌组织和重要血管的运动。
通过从呼吸运动存储库中选择的运动信息子集的稀疏线性组合生成 ROI 的运动先验，并且使用加权稀疏统计模型来保留局部呼吸运动细节。我们还采用了基于运动先验的配准方法来提高 ROI 中的运动估计精度，并设计了自适应变量系数，以在配准过程中交互权衡先验知识和图像强度信息的相对影响。
将所提出的方法应用于十个测试对象，以估计呼吸运动场。定量分析得出的目标配准误差平均值为 1.5（0.8）mm，平均对称面距离为 1.4（0.6）mm。
与传统方法相比，该方法在保留局部运动细节和减少 ROI 中的估计误差方面具有显著优势。这些结果还为文献中的肺呼吸运动建模提供了基准。
