Rohlfing Torsten, Denzler Joachim, Grässl Christoph, Russakoff Daniel B, Maurer Calvin R
Neuroscience Program at SRI International, 333 Ravenswood Avenue, Menlo Park, CA 94025-3493, USA.
IEEE Trans Med Imaging. 2005 Nov;24(11):1455-68. doi: 10.1109/TMI.2005.857651.
Accurate and fast localization of a predefined target region inside the patient is an important component of many image-guided therapy procedures. This problem is commonly solved by registration of intraoperative 2-D projection images to 3-D preoperative images. If the patient is not fixed during the intervention, the 2-D image acquisition is repeated several times during the procedure, and the registration problem can be cast instead as a 3-D tracking problem. To solve the 3-D problem, we propose in this paper to apply 2-D region tracking to first recover the components of the transformation that are in-plane to the projections. The 2-D motion estimates of all projections are backprojected into 3-D space, where they are then combined into a consistent estimate of the 3-D motion. We compare this method to intensity-based 2-D to 3-D registration and a combination of 2-D motion backprojection followed by a 2-D to 3-D registration stage. Using clinical data with a fiducial marker-based gold-standard transformation, we show that our method is capable of accurately tracking vertebral targets in 3-D from 2-D motion measured in X-ray projection images. Using a standard tracking algorithm (hyperplane tracking), tracking is achieved at video frame rates but fails relatively often (32% of all frames tracked with target registration error (TRE) better than 1.2 mm, 82% of all frames tracked with TRE better than 2.4 mm). With intensity-based 2-D to 2-D image registration using normalized mutual information (NMI) and pattern intensity (PI), accuracy and robustness are substantially improved. NMI tracked 82% of all frames in our data with TRE better than 1.2 mm and 96% of all frames with TRE better than 2.4 mm. This comes at the cost of a reduced frame rate, 1.7 s average processing time per frame and projection device. Results using PI were slightly more accurate, but required on average 5.4 s time per frame. These results are still substantially faster than 2-D to 3-D registration. We conclude that motion backprojection from 2-D motion tracking is an accurate and efficient method for tracking 3-D target motion, but tracking 2-D motion accurately and robustly remains a challenge.
在患者体内准确快速地定位预定义目标区域是许多图像引导治疗程序的重要组成部分。这个问题通常通过将术中二维投影图像与术前三维图像配准来解决。如果在干预过程中患者没有固定，在手术过程中会多次重复二维图像采集，那么配准问题就可以转化为三维跟踪问题。为了解决三维问题，我们在本文中提出应用二维区域跟踪来首先恢复投影平面内变换的分量。所有投影的二维运动估计被反投影到三维空间，然后在三维空间中将它们组合成三维运动的一致估计。我们将这种方法与基于强度的二维到三维配准以及二维运动反投影后接二维到三维配准阶段的组合方法进行比较。使用基于基准标记的金标准变换的临床数据，我们表明我们的方法能够根据在X射线投影图像中测量的二维运动在三维空间中准确跟踪椎骨目标。使用标准跟踪算法（超平面跟踪），可以在视频帧率下实现跟踪，但相对频繁地失败（在所有跟踪帧中，目标配准误差（TRE）优于1.2毫米的占32%，TRE优于2.4毫米的占82%）。使用基于归一化互信息（NMI）和模式强度（PI）的基于强度的二维到二维图像配准，准确性和鲁棒性有了显著提高。在我们的数据中，NMI跟踪了所有帧的82%，TRE优于1.2毫米，96%的所有帧TRE优于2.4毫米。这是以降低帧率为代价的，平均每帧和每个投影设备的处理时间为1.7秒。使用PI的结果稍微更准确，但平均每帧需要5.4秒。这些结果仍然比二维到三维配准快得多。我们得出结论，从二维运动跟踪进行运动反投影是跟踪三维目标运动的一种准确且高效的方法，但准确且稳健地跟踪二维运动仍然是一个挑战。
