Partial-to-Full Registration based on Gradient-SDF for Computer-Assisted Orthopedic Surgery

TL;DR

This paper introduces a fast, robust partial-to-full bone registration method based on gradient-SDF for computer-assisted orthopedic surgery, improving accuracy and efficiency over existing landmark-based techniques.

Contribution

The proposed gradient-SDF based registration framework offers rapid, accurate, and landmark-free bone registration suitable for real-time clinical applications.

Findings

Achieves convergence in less than 1 second

Provides mean target registration error as low as 2.198 mm

Robust to 90% outliers in data

Abstract

In computer-assisted orthopedic surgery (CAOS), accurate pre-operative to intra-operative bone registration is an essential and critical requirement for providing navigational guidance. This registration process is challenging since the intra-operative 3D points are sparse, only partially overlapped with the pre-operative model, and disturbed by noise and outliers. The commonly used method in current state-of-the-art orthopedic robotic system is bony landmarks based registration, but it is very time-consuming for the surgeons. To address these issues, we propose a novel partial-to-full registration framework based on gradient-SDF for CAOS. The simulation experiments using bone models from publicly available datasets and the phantom experiments performed under both optical tracking and electromagnetic tracking systems demonstrate that the proposed method can provide more accurate results than standard benchmarks and be robust to 90% outliers. Importantly, our method achieves convergence in less than 1 second in real scenarios and mean target registration error values as low as 2.198 mm for the entire bone model. Finally, it only requires random acquisition of points for registration by moving a surgical probe over the bone surface without correspondence with any specific bony landmarks, thus showing significant potential clinical value.