Now we consider robotic image guided intervention, which concentrates on visualization on the internal structures of a patient in order to guide a robotic device and/or its human operator. This is usually associated with interventional radiology and surgery, although the concepts described here could more broadly apply to any health care needs in which the patient can not be visualized naturally. No matter the application, such interventions require advances in image acquisition and analysis, robots developments that are compatible with imaging environments, and methods for the robots and their human operators to use the image data.
Sensor data are essential for building models and acquiring real time information during interventional radiology and surgery. Real time medical imaging techniques such as MRI (Magnetic Resonance Imaging), spectroscopy, ultrasound, and optical coherence tomography (OCT) can provide significant benefits when they enable the physician to see subsurface structures and/or tissue properties. Image acquired pre-operatively can be used for simulation and planning. New techniques such as elastography, which non-invasively quantifies tissue compliance, are needed in order to provide images that provide useful, quantitative physical information. The necessary speed and resolution of imager is not yet understood for robot control. We must determine how to integrate these with robotic systems to provide useful information to the surgeon and the robot to react to patient in real time.
One of the most useful forms of imaging is MRI (Magnetic Resonance Imaging). The MRI design compatible robots is especially challenging because MRI relies on a strong magnetic field and RF (Radio Frequency) pulses, and so it is not possible to use components that can interfere with, or be susceptible to, these physical effects. This rules out most components used for typical robots, such as ferromagnetic materials and electric motor. Interventional radiology or surgery inside an imager places severe constraints on robot size and geometry as well as the nature of the clinician robot interaction.
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