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Medical robotic systems have the potential to greatly enhance physician performance and improve patient care. These approaches can increase the speed and precision of medical procedures and enable new, less invasive procedures.
My research focuses on developing robot motion planning algorithms and physically-based simulations for medical applications, including robotic surgical assistance, treatment planning, medical image registration, and physician training.
My research spans the following areas:
- Motion Planning for Medical Robotics:
The objective of motion planning
in medical robotics is to compute actions that will guide a surgical device around
anatomical obstacles to reach a clinical target. We are developing new motion planning algorithms using optimization and sampling-based methods. These motion planning algorithms must address key challenges
that arise in medical applications, including deformations, uncertainty,
and optimality.
- Physically-based Medical Simulation:
Human soft tissues are heterogeneous and have nonlinear properties, resulting in complex deformations during clinical procedures.
Using finite element methods and mesh maintenance algorithms, we are developing simulations of soft tissues and their interaction with medical devices.
These simulations can be used for interactive physician training, procedure planning, and to assist in the registration of diagnostic and treatment images obtained at different times.
See research projects and publications for information about specific projects.
This research is supported by the National Science Foundation (NSF), the National Institutes of Health (NIH), and the Department of Defense (DOD).
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