Imaging Eyes in Motion: Dynamic Compensation with Robotic Optical Coherence Tomography
Motion is a longstanding challenge faced in ophthalmic imaging. Patients undergoing Optical Coherence Tomography (OCT) eye exams rely on mechanical stabilization to suppress motion during high-quality imaging, which is a significant barrier for patients with impaired mobility or movement disorders. We previously developed a mobile robotic OCT system to overcome this barrier. The system still requires subjects to adopt a stable posture, however, which precludes imaging during large-scale motion, such as in Parkinson's disease or physical activity. Thus in this work, we exploit the periodic motions commonly seen during in-place activity or movement disorders and use an autoregressive filter to predict motions 100s of ms into the future based on 5 s of historical observations. This allows the robotic system to learn, anticipate, and compensate for motion to maintain high image quality. Using this approach, we conducted experiments with phantoms and with two healthy human subjects performing movement tasks. Results show that compensation reduced eye errors below the tolerance of our scanner for ≥ 8 0% of subject imaging time. Our method demonstrates fast and robust motion prediction capabilities for subjects in various motion profiles, showing promising results for future research and clinical applications.
