ActiveReg: Information-Driven Active Regional Probing for Partial-to-Full Bone Registration

2026-10-07

AI Summary

This study addresses the limitations of full-bone registration in orthopedic surgery, which conventionally requires numerous probed points, is time-consuming, and is constrained by the exposed surgical field. To overcome these challenges, this work proposes a closed-loop, information-driven partial-to-full bone registration framework. The method replaces traditional single-point acquisition with probing region recommendations, employing a D-optimal region planner to achieve efficient sampling. Furthermore, an online evaluation mechanism based on innovation consistency is introduced, enabling real-time assessment of registration convergence without requiring ground truth. Both simulation and experimental results demonstrate that the proposed approach attains registration accuracy comparable to conventional methods using only approximately 25 probed points instead of over one thousand, thereby significantly enhancing intraoperative efficiency.

Abstract

Accurate bone registration is essential for orthopedic surgical navigation and robotic assistance. In a common workflow, the surgeon needs to identify and probe a number of prescribed locations on the exposed bone surface to obtain data points for registration, which can be difficult and time-consuming under limited surgical exposure. Reducing the number of required points while providing clear probing guidance would ease the surgeon's acquisition task intra-operatively. We present ActiveReg, a closed-loop framework that recommends probing regions rather than individual points, allowing flexibility in the exact contact location. A D-optimal planner that takes into account the already acquired points and the uncertainty of current registration information helps achieve accurate registration with fewer points. An online assessment combines pre-update innovation consistency with uncertainty at predefined surgical task locations improves the reliability of completion decision, without requiring ground truth. Simulations across four anatomical scenarios demonstrated that ActiveReg can achieve competitive accuracy compared with baseline registration methods while using substantially fewer acquired points. Real phantom experiments with optical and electromagnetic tracking demonstrated comparable mean target registration error to the Gradient-SDF framework, using only 24-28 acquired points instead of 771-1263.

Problem

• bone registration
• surgical navigation
• point reduction
• active probing
• partial-to-full registration

Innovation

• Active Regional Probing
• Partial-to-Full Registration
• D-optimal Planner
• Closed-loop Framework
• Online Assessment