Open-source navigation system for tracking dissociated parts with multi-registration

Purpose During reconstructive surgery, knee and hip replacements, and orthognathic surgery, small misalignments in the pose of prosthesis and bones can lead to severe complications. Hence, the translational and angular accuracies are critical. However, traditional image-based surgical navigation lacks orientation data between structures, and imageless systems are unsuitable for cases of deformed anatomy. We introduce an open-source navigation system using a multiple registration approach that can track instruments, implants, and bones to precisely guide the surgeon in emulating a preoperative plan. Methods We derived the analytical error of our method and designed a set of phantom experiments to measure its precision and accuracy. Additionally, we trained two classification models to predict the system reliability from fiducial points and surface matching registration data. Finally, to demonstrate the procedure feasibility, we conducted a complete workflow for a real clinical case of a patient with fibrous dysplasia and anatomical misalignment of the right femur using plastic bones. Results The system is able to track the dissociated fragments of the clinical case and average alignment errors in the anatomical phantoms of 1.08 +/- 0.68 mm and 1.49 +/- 1.19 degrees. While the fiducial-points registration showed satisfactory results given enough points and covered volume, we acknowledge that the surface refinement step is mandatory when attempting surface matching registrations. Conclusion We believe that our device could bring significant advantages for the personalized treatment of complex surgical cases and that its multi-registration attribute is convenient for intraoperative registration loosening cases.

Automated summary

Introduces an open-source navigation system using a multiple registration approach to track instruments, implants, and bones during surgery. The system's precision and accuracy were measured through analytical error derivation and phantom experiments. A reliability prediction model was also developed. A complete workflow for a clinical case was conducted to demonstrate the system's feasibility.