Biometric Navigation with Ultrasound

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Biometric Navigation with Ultrasound

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Title: Biometric Navigation with Ultrasound
Author: Schwartz, Benjamin Matthew
Citation: Schwartz, Benjamin Matthew. 2012. Biometric Navigation with Ultrasound. Doctoral dissertation, Harvard University.
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Abstract: We have designed and demonstrated a new class of medical navigation methods that use the fingerprint-like biometrically distinct ultrasound echo patterns produced by different locations in tissue. As an example of this new biometric navigation approach, we have constructed and tested a system that uses ultrasound data to achieve prospective motion compensation in MRI, especially for respiratory motion during interventional MRI procedures in moving organs such as the liver. The ultrasound measurements are collated with geometrical information from MRI during a training stage to form a mapping table that relates ultrasound measurements to positions. During prospective correction, the system makes frequent ultrasound measurements and uses the map to determine the corresponding position. Results in motorized linear motion phantoms and freely breathing animals indicate that the system performs well. Apparent motion is reduced by up to 97.8%, and motion artifacts are reduced or eliminated in 2D Spoiled Gradient-Echo images. The motion compensation is sufficient to permit MRI thermometry of focused ultrasound heating during respiratory-like motion, with results similar to those obtained in the absence of motion. This new technique may have applications for MRI thermometry and other dynamic imaging in the abdomen during free breathing. We have also extended this technique to situations in which external position information during training is unavailable or incomplete, by extending the concept of Simultaneous Localization and Mapping to include determining the topology of a dense motion path through a gaussian random field. In the course of these investigations, we have also developed modified forms of referenceless MRI thermometry and Kalman filtering, specially adapted to optimize accuracy under our experimental conditions.
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