Papers
16
Total Citations
233
H-Index
7
About
Kenan Niu is a pioneering researcher at the intersection of medical robotics, image-guided interventions, and intelligent control systems, with particular expertise in robotic catheters, ultrasound reconstruction, and surgical robotics. His work addresses fundamental challenges in minimally invasive cardiovascular and orthopedic procedures, where precision and safety are paramount. Niu's most influential contribution — his 2021 LSTM-based hysteresis modeling for robotic catheters (64 citations) — tackled a critical obstacle to precise catheter tip positioning in cardiovascular interventions. Building on this, his deep-learning-driven compliant motion control framework (40 citations) introduced intelligent force-aware steering to prevent dangerous vessel wall injuries during robotic catheter navigation. Equally impactful is Niu's body of work on robotic ultrasound systems. His research spanning calibration methodologies, automated 3D reconstruction frameworks, and pre-clinical spine surgery applications (collectively accumulating over 80 citations) has systematically advanced the reliability and clinical readiness of non-radiative imaging for orthopedic and spinal procedures. His augmented reality interface for robotic pedicle screw placement further demonstrates his commitment to bridging autonomous robotics with surgical workflows. With over 215 total citations and contributions spanning machine learning, surgical robotics, and medical imaging, Niu represents a distinctly interdisciplinary voice shaping the future of intelligent, image-guided surgical systems.
Research Focus
Key Achievements
Top Papers
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- 4A Framework for Fast Automatic Robot Ultrasound Calibration23 citations · 2021
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