Optimizing Fluoroscopic Imaging: Selecting Stable Vertebral Images and Path Tracking With Model Predictive Control

Fluoroscopic imaging plays a critical role in numerous medical procedures, particularly in interventional radiology and orthopedic surgery. This study presents a novel framework for optimizing fluoroscopic imaging by selecting stable vertebral projections and performing path tracking using Model Predictive Control (MPC). An O-Arm system model was developed, and its kinematic and dynamic properties were analyzed to enable accurate tracking of the desired surgical trajectory. MPC was employed to minimize tracking errors which can account for hardware and software constraints that may arise during intraoperative operation. To improve imaging quality, the desired surgical path was evaluated offline by incorporating the patient’s respiration pattern, which introduces vertebral displacement and can degrade fluoroscopic visualization. By automating O-Arm motion to compensate for these respiratory-induced variations, the proposed method aims to enhance imaging stability and improve surgical outcomes. Prior to surgery, virtual radiographic images were generated using Digitally Reconstructed Radiographs (DRRs) derived from the patient’s 3D CT data and ventilator-driven respiratory motion. Each frame was analyzed to identify the views exhibiting minimal variation, enabling the determination of an optimal end-effector trajectory for the O-Arm. The results show that, despite certain approximations in the methodology, the mean pixel displacement of the region of interest improved substantially from 37.59 pixels in the fixed-view scenario to 0.54 pixels using the proposed dynamic approach.