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首页> 外文会议>SPIE Conference on Biomedical Applications in Molecular, Structural, and Functional Imaging >Lung Registration Using Airway Tree Morphometry
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Lung Registration Using Airway Tree Morphometry

机译：肺部注册使用气道树形态学

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This paper describes a non-linear medical image registration algorithm that aligns lung CT images scanned at different respiratory phases. The method uses landmarks obtained from the airway tree to find the airway branch extension lines and where the lines intersect the lung surface. The branch extension and lung intersection voxels on the surface were the crucial landmarks that initialize the non-rigid registration process. The advantage of these landmarks is that they have high correspondence between the matching patterns in the template images and deformed images. This method was developed and tested on CT examinations from participants in an asthma study. The registration accuracy was evaluated by the average distance between the corresponding airway tree branch points in the pair of images. The mean value of the distance between landmarks in template images and deformed matching images for subjects 1 and 2 were 8.44 mm (±4.46 mm) and 4.33 mm (± 3.78 mm), respectively. The results show that the lung image registration technique developed in this study may prove useful in quantifying longitudinal changes, performing regional analysis, tracking lung tumors, and compensating for subject motion across CT images.
机译：本文介绍了一种非线性医学图像配准算法，其对准在不同呼吸阶段扫描的肺CT图像。该方法使用从气道树中获得的地标，找到气道分支延伸线，线与肺表面相交的地方。表面上的分支延伸和肺交叉口体素是初始化非刚性登记过程的重要地标。这些地标的优点在于它们在模板图像中的匹配模式和变形图像中具有高对应关系。在哮喘研究中的参与者的CT检查中开发并测试了该方法。通过在一对图像中的相应气道树分支点之间的平均距离来评估注册精度。模板图像中地标与受试者1和2的变形匹配图像之间的平均值分别为8.44mm（±4.46mm）和4.33mm（±3.78mm）。结果表明，该研究中开发的肺部图像登记技术可用于量化纵向变化，进行区域分析，跟踪肺肿瘤，并在CT图像上进行对象运动。

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