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Validation of a two- to three-dimensional registration algorithm for aligning preoperative CT images and intraoperative fluoroscopy images.

机译:二维至三维配准算法的验证,以对齐术前CT图像和术中透视图像。

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摘要

We present a validation of an intensity based two- to three-dimensional image registration algorithm. The algorithm can register a CT volume to a single-plane fluoroscopy image. Four routinely acquired clinical data sets from patients who underwent endovascular treatment for an abdominal aortic aneurysm were used. Each data set was comprised of two intraoperative fluoroscopy images and a preoperative CT image. Regions of interest (ROI) were drawn around each vertebra in the CT and fluoroscopy images. Each CT image ROI was individually registered to the corresponding ROI in the fluoroscopy images. A cross validation approach was used to obtain a measure of registration consistency. Spinal movement between the preoperative and intraoperative scene was accounted for by using two fluoroscopy images. The consistency and robustness of the algorithm when using two similarity measures, pattern intensity and gradient difference, was investigated. Both similarity measures produced similar results. The consistency values were rotational errors below 0.74 degree and in-plane translational errors below 0.90 mm. These errors approximately relate to a two-dimensional projection error of 1.3 mm. The failure rate was less than 8.3% for three of the four data sets. However, for one of the data sets a much larger failure rate (28.5%) occurred.
机译:我们提出了基于强度的二维到三维图像配准算法的验证。该算法可以将CT体积配准到单平面荧光检查图像。使用了接受腹主动脉瘤血管内治疗的患者的四个常规临床数据集。每个数据集包括两个术中透视图像和术前CT图像。在CT和透视图像中,在每个椎骨周围绘制感兴趣区域(ROI)。每个CT图像ROI分别与透视图像中的相应ROI配准。交叉验证方法用于获得注册一致性的度量。术前和术中场景之间的脊髓运动是通过使用两个透视图像来解决的。研究了使用两种相似度(图案强度和梯度差)时算法的一致性和鲁棒性。两种相似性度量产生相似的结果。一致性值是低于0.74度的旋转误差和低于0.90 mm的面内平移误差。这些误差大约与1.3mm的二维投影误差有关。四个数据集中的三个数据集的故障率均小于8.3%。但是,对于其中一个数据集,发生故障的概率更大(28.5%)。

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