PURPOSE 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. METHODS 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. RESULTS 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. CONCLUSIONS 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.

中文翻译：

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基于统计的基于稀疏的局部肺运动建模方法，用于模型驱动的肺活检。

目的肺活检是目前诊断癌症最有效的方法。然而，呼吸引起的位置不确定性对精确的肺活检提出了挑战。为了减少运动建模的医学图像需求，在本研究中，从整个胸部计算机断层扫描（CT）和CT透视检查中提取感兴趣区域（ROI）中的局部肺运动信息，以预测潜在癌组织的运动。模型驱动的肺活检过程中的重要血管。方法通过将来自呼吸运动存储库的运动信息的子集进行稀疏线性组合，生成ROI的先验运动，然后使用基于加权的稀疏统计模型来保存局部呼吸运动细节。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，以交互式权衡配准过程中先验知识和图像强度信息的相对影响。结果该方法被应用于十个测试对象的呼吸运动场估计。定量分析的平均目标配准误差为1.5（0.8）mm，平均对称表面距离为1.4（0.6）mm。结论与传统方法相比，该方法在保留局部运动细节和减少ROI估计误差方面显示出显着优势。这些结果也为文献中的肺呼吸运动建模提供了基准。