Worcester Polytechnic Institute, 100 Institute Rd, Worcester, MA, 01609, USA.
Dartmouth College Dartmouth-Hitchcock Medical Center, 1 Medical Center Dr, Lebanon, NH, 03766, USA.
Int J Comput Assist Radiol Surg. 2021 Jun;16(6):943-953. doi: 10.1007/s11548-021-02395-0. Epub 2021 May 10.
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.
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.
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.
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.
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，从而导致显著的配准误差。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，实现了对脊柱姿势大变化具有鲁棒性的分级椎体配准。
我们使用离体猪脊柱和活猪来开发和测试我们的技术。每个样本在初始位置用术前 CT（pCT）扫描，然后在不同的手术体位用术中立体视觉（iSV）重新扫描。患者配准通过参数化运动框架方法将任意脊柱姿势 pCT 和 iSV 校正到共同的中立姿势。然后生成拓扑编码深度投影 2D 图像，以建立可翻转的点到点对应关系。通过 2D 图像配准生成 pCT 和 iSV 椎体表面之间的分级点对应关系。最后，通过直接映射 3D 表面点对获得椎体分级刚性配准。植入的微型螺钉用作基准标记来测量配准精度。
在七具离体猪脊柱和两次活体动物手术中（所有脊柱的最大脊柱内姿势变化为 87.5 毫米和 32.7 度平均值），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，然后再额外 30 秒进行深度图像投影和分级配准。
所提出的分级脊柱配准的准确性和效率支持其在人类开放性脊柱手术中的应用。该配准框架本身也可能适用于其他术中成像模式，如超声和 MRI，这可能会扩大该方法在脊柱配准中的应用范围。
