Mark Renfrew
Papers
3
Total Citations
24
H-Index
3
About
Mark Renfrew’s research lies at the critical intersection of medical robotics and intelligent image-guided intervention, where precision and safety are paramount. His most influential work focuses on **active localization**—developing probabilistic methods that allow robotic systems to autonomously control imaging to track both a surgical needle and its target in real time. His 2017 paper on this topic, with 12 citations, and a foundational 2013 paper (9 citations) introduced particle-filter-based frameworks that dynamically adjust imaging parameters, significantly enhancing accuracy in procedures like biopsies. More recently, Renfrew has tackled the pressing challenge of **adverse and anomalous event detection** in medical robots. His 2021 work (3 citations) describes a system that not only detects but also predicts dangerous events, incorporating novel simulation, data collection, and user interface tools specifically designed for small animal biopsy robots. This dual focus—on both active guidance and proactive safety—positions Renfrew as a key contributor to the next generation of autonomous, fail-safe surgical systems.
Research Focus
Key Achievements
Top Papers
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