Patient-Specific Depth Error Correction in Dual-Sensor System for Image-Guided Minimally Invasive Facial Osteotomy

Abstract

This study introduces a patient-specific method for real-time depth error correction, improving the accuracy of the dual-sensor system previously developed by our team for image-guided facial osteotomy. The system integrates two RGB-D sensors with time-of-flight (ToF) technology and an optical localizer, enabling simultaneous real-time tracking of both the patient and surgical tools. Due to anatomical variations in facial skin, ToF sensors are susceptible to depth inaccuracies, requiring a tailored correction strategy. The proposed method uses the probe's tip position, tracked by the optical localizer, to estimate parameters for an error model and correct depth inaccuracies. This real-time, patient-specific depth error correction significantly improves both registration and shape sensing accuracy. Validations using three patient mock-ups demonstrated a 87% reduction in depth errors, with an average fiducial registration accuracy of 2.33±0.23mm and an average shape sensing accuracy of 2.25±0.38mm. These results highlight the potential of patient-specific depth error correction to improve registration accuracy, thereby ensuring reliable and precise guidance, which may improve clinical outcomes in minimally invasive facial osteotomies.