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Robotic Craniomaxillofacial Osteotomy System Using Acoustic 3D Registration

Jiayu Zhu, Runzhe Han, Mengning Yuan, Bimeng Jie, Shanshan Du, Yang He, Runshi Zhang, Junchen Wang

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Abstract

Osteotomy holds a pivotal position among the fundamental procedures in craniomaxillofacial (CMF) surgery. However, there are inherent challenges and risks associated with ensuring the recuperation of occlusion, safeguarding the facial nerves and blood vessels, as well as preserving facial aesthetics. In this study, a non-invasive image-to-patient reg- istration method for navigation/robotic CMF surgery based on intraoperative freehand ultrasound (US) 3D reconstruction is proposed. Building upon this, a CMF osteotomy robotic system with compliant human-robot interaction and osteotomy trajectory planning was devised. In the freehand US 3D recon- struction and registration experiments, the registration errors for human volunteers and phantoms were consistently less than 1 mm. In robot osteotomy experiments based on the resulting registration, the average osteotomy error was below 1.5 mm. The proposed US 3D reconstruction based registration method is non-invasive and radiation-free, and shows the promising accuracy which is suitable for CMF robotic or navigation systems.

Index terms

Medical Robots and Systems Deep Learning Methods Compliance and Impedance Control