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首页> 外文会议>Conference on physiology and function from multidimensional images >3D warping and registration from lung images
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3D warping and registration from lung images

机译：3D扭曲和注册肺图像

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Computerized volumetric warping and registration of 3D lung images can provide objective, accurate, and reproducible measures to the understanding of human lung structure and function. It is also invaluable to the assessment of the presence of diseases and their response to therapy. However, due to the complexity of breathing motion, little work has been carried out in this research area. In this paper, we propose a novel scheme to implement volumetric lung warping and registration from 3D CT images obtained at different stages of breathing. Bronchial points of airway trees and vessels are selected as feature points since they can be easily tracked over consecutive frames. The warping of these feature points into the entire volume is obtained based on a model of continuum mechanics and is implemented in an iteration fashion governed by such model. The model consists of three constraints: an incompressibility constraint, a divergence-free constraint and a motion-discontinuity- preserving smoothness constraint. An objective function is defined as a weighted sum of the three constraint terms and the desired displacement field of the whole volume between different stage of breathing is obtained by minimizing this objective function. The 3D warping is therefore represented by the dense displacement field obtained from the iteration. Preliminary results are visualized by overlaying the displacement field with the original images. Effectiveness of the algorithm is also evaluated by comparing the volume difference between the real and warped volumes. We believe the proposed approach will open up several areas of research in lung image analysis that can make use of the results from warping lung volumes.
机译：3D肺图像的计算机化体积翘曲和登记可以提供对人肺结构和功能的理解提供客观，准确和可重复的措施。对评估疾病的存在及其对治疗的反应也非常宝贵。然而，由于呼吸运动的复杂性，这项研究领域已经进行了一点工作。在本文中，我们提出了一种新颖的方案来实现在不同呼吸阶段获得的3D CT图像中的体积肺部翘曲和注册。呼气道树木和船只的支气管点被选为特征点，因为它们可以在连续框架上轻松跟踪。基于连续式机制的模型获得这些特征点的翘曲，并以由这种模型控制的迭代方式实施。该模型由三个约束组成：不可压缩的限制，一个无分歧约束和运动不连续性保留的平滑度约束。目标函数被定义为三个约束术语的加权和，通过最小化该目标函数来获得不同呼吸阶段之间的整个体积的所需位移场。因此，3D翘曲由从迭代中获得的致密位移场表示。通过将位移场与原始图像覆盖来可视化初步结果。还通过比较真实和翘曲的卷之间的体积差异来评估算法的有效性。我们认为，拟议的方法将开辟肺部图像分析研究的几个研究领域，可以利用翘曲肺量的结果。

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