Cross-Modality Registration using Bone Surface Pointcloud for Robotic Ultrasound-Guided Spine Surgery.

Journal of medical robotics research Pub Date : 2025-03-01 Epub Date: 2025-01-17 DOI:10.1142/s2424905x25400045
Xihan Ma, Xiao Zhang, Yang Wang, Christopher J Nycz, Arno Sungarian, Songbai Ji, Xinming Huang, Haichong K Zhang
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Abstract

Image guidance using preoperative magnetic resonance imaging (MRI) and intraoperative ultrasound (US) can improve the outcome of spine surgery. Employing a robotic US system (RUSS) allows the automated acquisition of large 3D US volumes, facilitating accurate registration. However, such registration remains challenging due to the cross-modality discrepancy. To address this issue, we present a pipeline that extracts spine pointclouds from MRI and 3D US to perform per-vertebra registration. Experiments showed a registration accuracy of 1.82 mm in terms of residual root mean square error and 7.02 mm in terms of Chamfer distance. The pipeline exhibits superior robustness to suboptimal initial conditions compared with the two baseline methods. It also demonstrated good time efficiency under real-time conditions, demonstrating the potential applicability in RUSS-guided spine surgeries.

利用骨表面点云进行机器人超声引导脊柱手术的交叉模态配准。
术前磁共振成像(MRI)和术中超声(US)的图像引导可以改善脊柱手术的预后。采用机器人美国系统(RUSS)可以自动获取大型3D美国体积,促进准确注册。然而,由于跨模态的差异,这种注册仍然具有挑战性。为了解决这个问题,我们提出了一个从MRI和3D US中提取脊柱点云的管道来进行椎体配准。实验表明,该方法的配准精度为残差均方根误差1.82 mm,倒角距离7.02 mm。与两种基线方法相比,该方法对次优初始条件具有更强的鲁棒性。在实时条件下也表现出良好的时间效率,显示了在russ引导下脊柱手术中的潜在适用性。
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