On May 30th 2024 at 15:00, Taylor Frantz will defend their PhD entitled “AUGMENTED REALITY IN SURGERY ON THE DEVELOPMENT OF REAL-TIME INTERVENTIONAL PLANNING AND NAVIGATION FOR NEUROSURGICAL AND ORTHOPEDIC USE CASES: BENCH-TOP TO CLINICAL EVALUATION”.

Everybody is invited to attend the presentation in room D.2.01, or digitally via this link.

Computer aided navigation (CAN) is a surgical technology which allows a surgeon to use patient medical image data as map to guide the procedure. It comprises several interconnected processes: visualization of 3D medical image data, tracking of surgical instrumentation, definition of a virtual coordinate system around the patient, and the alignment of the image data to the patient.

Despite quantitative benefits, the technology is often not used due to size, cost, and unintuitive visualization of 3D patient data as 2D black and white image. Augmented reality (AR) devices often integrate requisite hardware for CAN into a compact and mobile head mounted device (HMD) and allow the surgeon to view complex 3D data as a hologram overlying the patient. This work addresses technical limitations of such low-cost AR hardware with respect to tracking performance and presents evidence supporting their use in both neurosurgical and orthopedic domains.

Building on early work in videometric tracking as a proof-of-concept, the development of monocular infrared (IR) tracking of existing surgical instrumentation provided a method to establish a room-stable coordinate system and a mechanism for precise user input; both required for CAN. This tracking solution was validated in a VICON motion capture lab and demonstrated a mean pose estimation error of 0.78 mm ± 0.74 mm and 0.84° ± 0.64°.

Following this, phantom trials in navigated external ventricular drain (EVD) placement, and total shoulder and hip arthroplasty were performed. The results demonstrated a reduction in technique learning curve of the former, and improved outcomes of the latter when compared to traditional non-navigated techniques. Moreover, AR data registration was found to be comparable to modern CAN systems.

Clinical trials in both tumor resection planning and EVD were then performed to assess efficacy of AR-CAN compared to current surgical practice. In the former, AR-CAN demonstrated a reduction in preoperative planning time with superior lesion delineation when compared to neuronavigation. Preliminary results in AR navigated EVD placement outcomes demonstrate 82% optimal (grade I), 18% sub optimal (grade II), and 0% (grade III). This currently outperforms literature, given single attempt insertion.