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Statistical Deformable Model-Based Reconstruction of a Patient-Specific Surface Model from Single Standard X-ray Radiograph

机译:基于统计可变形模型的单个标准X射线射线照相重建患者特定表面模型

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摘要

In this paper, we present a hybrid 2D-3D deformable registration strategy combining a landmark-to-ray registration with a statistical shape model-based 2D-3D reconstruction scheme, and show its application to reconstruct a patient-specific 3D surface model of the pelvis from single standard X-ray radiograph. The landmark-to-ray registration is used to find an initial scale and an initial rigid transformation between the X-ray image and the statistical shape model. The estimated scale and rigid transformation are then used to initialize the statistical shape model-based 2D-3D reconstruction scheme, which combines statistical instantiation and regularized shape deformation with an iterative image-to-model correspondence establishing algorithm. Quantitative and qualitative results of a feasibility study on clinical and cadaveric datasets are given, which indicate the validity of our approach.
机译:在本文中,我们提出了一种混合的2D-3D可变形配准策略,该策略将路标到射线配准与基于统计形状模型的2D-3D重建方案相结合,并展示了其在重建患者特定的3D表面模型中的应用单个标准X射线照相的骨盆。界标到射线的配准用于在X射线图像和统计形状模型之间找到初始比例和初始刚性变换。然后,使用估计的比例和刚性变换来初始化基于统计形状模型的2D-3D重建方案,该方案将统计实例化和规则化形状变形与迭代的图像到模型对应关系建立算法结合在一起。给出了关于临床和尸体数据集可行性研究的定量和定性结果,表明了我们方法的有效性。

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