基于椭圆曲线的c臂跟踪与重建。

Gouthami Chintalapani, Ameet K Jain, David H Burkhardt, Jerry L Prince, Gabor Fichtinger
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引用次数: 11

摘要

c臂透视在当代外科手术中无处不在,但由于难以获得x射线图像在三维空间中的位姿,c臂透视缺乏准确重建三维信息的能力。我们提出了一个统一的数学框架来解决术中姿态估计,对应和重建的问题,使用简单的椭圆曲线。与其他基于基准的跟踪方法相比,我们的方法使用单个椭圆来约束c臂姿态的6个自由度中的5个,以及来自两个图像/视图的成像体积中随机分布的未知点(自然存在或由图像空间中随机放置的珠子或其他标记引起),以完全恢复c臂姿态。初步的模拟实验表明,c型臂的平均跟踪精度为0.51°和0.12°STD。由于该方法使用简单的椭圆基准和患者信息,并且对工作空间的干扰非常小,因此该方法似乎足够精确,并且对许多临床应用具有吸引力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
CTREC: C-arm Tracking and Reconstruction using Elliptic Curves.

C-arm fluoroscopy is ubiquitous in contemporary surgery, but it lacks the ability to accurately reconstruct three-dimensional information, attributable to the difficulty in obtaining the pose of X-ray images in 3D space. We propose a unified mathematical framework to address the issues of intra-operative pose estimation, correspondence and reconstruction, using simple elliptic curves. In contrast to other fiducial-based tracking methods, our method uses a single ellipse to constrain 5 out of 6 degrees of freedom of C-arm pose, along with randomly distributed unknown points in the imaging volume (either naturally present or induced by randomly placed beads or other markers in the image space) from two images/views to completely recover the C-arm pose. Preliminary phantom experiments indicate an average C-arm tracking accuracy of 0.51° and 0.12° STD. The method appears to be sufficiently accurate and appealing for many clinical applications, since it uses a simple elliptic fiducial coupled with patient information and has very minimal interference with the workspace.

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