Image-guided robotic assistance to improve precision and reduce x-ray dose in orthopedic surgery for pediatric SCFE

Project Summary Placement of orthopaedic implants, such as guidewires and screws, is a common surgical task, and accurate placement is critical to the success of a range of orthopaedic procedures. However, the rate of screw misplacement is highly variable, with one study showing a 14% misplacement rate in the spine. While screws are often placed during open surgery, allowing

direct visualization of bones, there is a growing trend toward percutaneous screw placement. The most studied procedure has been pedicle screw placement, where a meta-analysis showed that a significant percentage of screws are misplaced and that image guidance can improve placement accuracy. Misplaced screws can put adjacent nerves, vessels, and joints at risk of

injury, leading to complications and revision surgery, which are often invasive and costly. The goal of this R01 proposal is to develop, evaluate, and prepare regulatory paperwork for first clinical studies of a new technique for guiding implant placement in orthopaedic surgery. The technique combines recent advancements in a low-profile robot with emerging methods for

image reconstruction and registration using a low-dose 3D cone-beam tomosynthesis (CBT) system. Our long-term goal is to provide a complete solution for precision implant planning, placement, and verification while minimizing radiation exposure to the patient and surgical staff. This new methodology would allow surgeons to accurately place implants with fewer images,

reduce radiation exposure, and provide verification of device placement in the operating room (OR), with opportunity for immediate revision. Our specific aims are to: 1. Develop a surgical navigation workstation based on the open-source software package 3D Slicer to integrate a low-profile guidance robot with 2D and 3D imaging for trajectory

planning and robotic guidance of screw placement. 2. Develop and translate low-dose CBT for SCFE by identifying imaging protocols, optimizing reconstruction quality, and segmenting target structures using combined model-based and deep-learning techniques. 3. Develop and translate a 3D to 2D image registration and guidance system that exploits the

rotating source to work off of a static C-arm gantry pose and track surgical instruments with respect to anatomy. 4. Integrate the technology developed in Specific Aims 1-3 and evaluate in phantom and cadaver studies. Modify as necessary to achieve less than 3 mm end-to-end targeting error. 5. Pursue an IDE submission to the FDA for first-in-human use in future work.