Purposes

Accurate and efficient spine registration is crucial to success of spine image guidance. However, changes in spine pose cause intervertebral motion that can lead to significant registration errors. In this study, we develop a geometrical rectification technique via nonlinear principal component analysis (NLPCA) to achieve level-wise vertebral registration that is robust to large changes in spine pose.

Methods

We used explanted porcine spines and live pigs to develop and test our technique. Each sample was scanned with preoperative CT (pCT) in an initial pose and rescanned with intraoperative stereovision (iSV) in a different surgical posture. Patient registration rectified arbitrary spinal postures in pCT and iSV into a common, neutral pose through a parameterized moving-frame approach. Topologically encoded depth projection 2D images were then generated to establish invertible point-to-pixel correspondences. Level-wise point correspondences between pCT and iSV vertebral surfaces were generated via 2D image registration. Finally, closed-form vertebral level-wise rigid registration was obtained by directly mapping 3D surface point pairs. Implanted mini-screws were used as fiducial markers to measure registration accuracy.

Results

In seven explanted porcine spines and two live animal surgeries (maximum in-spine pose change of 87.5 mm and 32.7 degrees averaged from all spines), average target registration errors (TRE) of 1.70 ± 0.15 mm and 1.85 ± 0.16 mm were achieved, respectively. The automated spine rectification took 3–5 min, followed by an additional 30 secs for depth image projection and level-wise registration.

Conclusions

Accuracy and efficiency of the proposed level-wise spine registration support its application in human open spine surgeries. The registration framework, itself, may also be applicable to other intraoperative imaging modalities such as ultrasound and MRI, which may expand utility of the approach in spine registration in general.

中文翻译：

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用于考虑大姿势变化的水平脊柱配准框架

目的

准确高效的脊柱配准对于脊柱图像引导的成功至关重要。然而，脊柱姿势的变化会导致椎间运动，从而导致严重的配准错误。在本研究中，我们通过非线性主成分分析（NLPCA）开发了一种几何校正技术，以实现对脊柱姿势的大幅变化具有鲁棒性的水平椎骨配准。

方法

我们使用外植的猪脊椎和活猪来开发和测试我们的技术。每个样本均在初始姿势下使用术前 CT (pCT) 进行扫描，并在不同的手术姿势下使用术中立体视觉 (iSV) 进行重新扫描。患者登记通过参数化移动框架方法将 pCT 和 iSV 中的任意脊柱姿势纠正为常见的中立姿势。然后生成拓扑编码的深度投影二维图像以建立可逆的点到像素对应关系。pCT 和 iSV 椎体表面之间的水平点对应关系是通过 2D 图像配准生成的。最后，通过直接映射 3D 表面点对获得封闭式椎体水平刚性配准。植入的微型螺钉用作基准标记来测量配准精度。

结果

在七个移植的猪脊柱和两个活体动物手术中（所有脊柱的最大脊柱内姿势变化为 87.5 毫米和 32.7 度），平均目标配准误差 (TRE) 分别为 1.70 ± 0.15 毫米和 1.85 ± 0.16 毫米。自动脊柱校正需要 3-5 分钟，然后再花费 30 秒进行深度图像投影和水平配准。

结论

所提出的水平脊柱配准的准确性和效率支持其在人体开放脊柱手术中的应用。配准框架本身也可以适用于其他术中成像模式，例如超声和 MRI，这通常可以扩展该方法在脊柱配准中的实用性。