Model-based spinal deformation compensation in robot-assisted decompressive laminectomy

Abstract

Decompressive laminectomy is a common operation for the treatment of lumbar spinal stenosis. A high-speed burr and piezosurgery are often used to remove the lamina to relieve compressed nerves. However, surgeons need to control the cutting margin of the lamina based on experience, and the remaining laminar thickness is difficult to quantify before image measurements. In other surgeries, robot-assisted grinding guided by navigation can make operations accurate, but the deformation of bone tissue caused by instrument interaction cannot be ignored, especially for spinal surgery. Because of the elasticity of the intervertebral disk, the spine deforms under external force, which leads to deviation from the preoperative trajectory for the robot. This paper proposes a model-based compensation method for spinal deformation during decompression operation. A torsion model and bending model are built based on traditional elastic mechanics, and then a coupling model is simplified and modified for improved robot control. The source and influence of residual error caused by the noise is analysed, and data-based estimation of the model parameters is conducted to accommodate different patients. Robot deformation is also considered in the control system, and the model inputs—the feedback force data—are processed using the adaptive Kalman filter in real time. The lumbar data are segmented for finite element analysis, which provides the input and output data with which the parameters of the model are estimated, and the simulation shows that the signal-to-noise ratio of the force sensor has a great effect on system performance.