2D-3D Reconstruction-Based Planning of Total Hip Arthroplasty

A review of advances in image-guided orthopedic surgery

Orthopedic surgery remains technically demanding due to the complex anatomical structures and cumbersome surgical procedures. The introduction of image-guided orthopedic surgery (IGOS) has significantly decreased the surgical risk and improved the operation results. This review focuses on the application of recent advances in artificial intelligence (AI), deep learning (DL), augmented reality (AR) and robotics in image-guided spine surgery, joint arthroplasty, fracture reduction and bone tumor resection. For the pre-operative stage, key technologies of AI and DL based medical image segmentation, 3D visualization and surgical planning procedures are systematically reviewed. For the intra-operative stage, the development of novel image registration, surgical tool calibration and real-time navigation are reviewed. Furthermore, the combination of the surgical navigation system with AR and robotic technology is also discussed. Finally, the current issues and prospects of the IGOS system are discussed, with the goal of establishing a reference and providing guidance for surgeons, engineers, and researchers involved in the research and development of this area.

Fracture reduction planning and guidance in orthopaedic trauma surgery via multi-body image registration

PURPOSES

Surgical reduction of pelvic fracture is a challenging procedure, and accurate restoration of natural morphology is essential to obtaining positive functional outcome. The procedure often requires extensive preoperative planning, long fluoroscopic exposure time, and trial-and-error to achieve accurate reduction. We report a multi-body registration framework for reduction planning using preoperative CT and intraoperative guidance using routine 2D fluoroscopy that could help address such challenges.

METHOD

The framework starts with semi-automatic segmentation of fractured bone fragments in preoperative CT using continuous max-flow. For reduction planning, a multi-to-one registration is performed to register bone fragments to an adaptive template that adjusts to patient-specific bone shapes and poses. The framework further registers bone fragments to intraoperative fluoroscopy to provide 2D fluoroscopy guidance and/or 3D navigation relative to the reduction plan. The framework was investigated in three studies: (1) a simulation study of 40 CT images simulating three fracture categories (unilateral two-body, unilateral three-body, and bilateral two-body); (2) a proof-of-concept cadaver study to mimic clinical scenario; and (3) a retrospective clinical study investigating feasibility in three cases of increasing severity and accuracy requirement.

RESULTS

Segmentation of simulated pelvic fracture demonstrated Dice coefficient of 0.92±0.06. Reduction planning using the adaptive template achieved 2-3 mm and 2-3° error for the three fracture categories, significantly better than planning based on mirroring of contralateral anatomy. 3D-2D registration yielded ~2 mm and 0.5° accuracy, providing accurate guidance with respect to the preoperative reduction plan. The cadaver study and retrospective clinical study demonstrated comparable accuracy: ~0.90 Dice coefficient in segmentation, ~3 mm accuracy in reduction planning, and ~2 mm accuracy in 3D-2D registration.

CONCLUSION

The registration framework demonstrated planning and guidance accuracy within clinical requirements in both simulation and clinical feasibility studies for a broad range of fracture-dislocation patterns. Using routinely acquired preoperative CT and intraoperative fluoroscopy, the framework could improve the accuracy of pelvic fracture reduction, reduce radiation dose, and could integrate well with common clinical workflow without the need for additional navigation systems.