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

Xihan Ma, Xiao Zhang, Christopher J. Nycz, Arno Sungarian, Songbai Ji, Xinming Huang, Haichong K. Zhang

Year
2024
Citations
4

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.

Keywords

Modality (human–computer interaction)MedicineUltrasoundRadiologySurgeryComputer scienceArtificial intelligence

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