Surgical Technologies Lab, Centre for Hip Health and Mobility, Biomedical Engineering, University of British Columbia, H.N. Ho Research Centre, 5th Floor, 2635 Laurel St, Vancouver, BC, V5Z 1M9, Canada.
Biomedical Engineering, Civil Engineering, McCaig Institute for Bone and Joint Health, University of Calgary, 2500 University Dr NW, Calgary, AB, T2N 1N4, Canada.
Int J Comput Assist Radiol Surg. 2019 Oct;14(10):1725-1739. doi: 10.1007/s11548-019-02024-x. Epub 2019 Jul 10.
Although multiple algorithms have been reported that focus on improving the accuracy of 2D-3D registration techniques, there has been relatively little attention paid to quantifying their capture range. In this paper, we analyze the capture range for a number of variant formulations of the 2D-3D registration problem in the context of pedicle screw insertion surgery.
We tested twelve 2D-3D registration techniques for capture range under different clinically realistic conditions. A registration was considered as successful if its error was less than 2 mm and 2° in 95% of the cases. We assessed the sensitivity of capture range to a variety of clinically realistic parameters including: X-ray contrast, number and configuration of X-rays, presence or absence of implants in the image, inter-subject variability, intervertebral motion and single-level vs multi-level registration.
Gradient correlation + Powell optimizer had the widest capture range and the least sensitivity to X-ray contrast. The combination of 4 AP + lateral X-rays had the widest capture range (725 mm). The presence of implant projections significantly reduced the registration capture range (up to 84%). Different spine shapes resulted in minor variations in registration capture range (SD 78 mm). Intervertebral angulations of less than 1.5° had modest effects on the capture range.
This paper assessed capture range of a number of variants of intensity-based 2D-3D registration algorithms in clinically realistic situations (for the use in pedicle screw insertion surgery). We conclude that a registration approach based on the gradient correlation similarity and the Powell's optimization algorithm, using a minimum of two C-arm images, is likely sufficiently robust for the proposed application.
虽然已经有多种算法被报道，旨在提高 2D-3D 配准技术的准确性，但很少有研究关注量化它们的捕获范围。在本文中，我们分析了在椎弓根螺钉插入手术背景下多种 2D-3D 配准问题变体形式的捕获范围。
我们在不同临床现实条件下测试了 12 种 2D-3D 配准技术的捕获范围。如果配准的误差在 95%的情况下小于 2 毫米和 2°，则认为其是成功的。我们评估了捕获范围对多种临床现实参数的敏感性，包括：X 射线对比度、X 射线数量和配置、图像中是否存在植入物、受试者间变异性、椎间运动以及单级与多级配准。
梯度相关+Powell 优化器具有最宽的捕获范围和对 X 射线对比度的最低敏感性。4 个 AP 加侧位 X 射线的组合具有最宽的捕获范围（725 毫米）。植入物投影的存在显著降低了配准的捕获范围（最多 84%）。不同的脊柱形状导致配准捕获范围略有变化（标准偏差为 78 毫米）。小于 1.5°的椎间角对捕获范围有适度影响。
本文评估了在临床现实情况下（用于椎弓根螺钉插入手术）多种基于强度的 2D-3D 配准算法变体的捕获范围。我们得出结论，基于梯度相关相似度和 Powell 优化算法的配准方法，使用最少两个 C 臂图像，对于所提出的应用可能具有足够的鲁棒性。
