UBC Theses and Dissertations
Ultrasound registration and tracking for robot-assisted laproscopic surgery Yip, Michael Chak Luen
In the past two decades, there has been considerable research interest in medical image registration during surgery. The overlay of medical images over the images from a surgical camera allows the surgeon to see sub-surface features such as tumor boundaries and vasculature. Ultrasound imaging is a prime candidate for medical image registration, as it is a real-time imaging modality and therefore is commonly-used for intraoperative surgical guidance. Prior technologies that attempted ultrasound-based registration have used external trackers in order to establish a geometric correspondence between the surgical cameras and the ultrasound probes; this requires probe and camera calibration, which is time-consuming, requires additional equipment, and adds additional sources of error to the registration. Another problem is how to maintain a registration between the ultrasound image and the underlying tissues, since tissues will move and deform from patient breathing and heartbeat, and from surgical instrument interaction with tissues. In order to overcome this, the underlying tissue should be tracked, and previously acquired ultrasound images should be registered and moved with the tracked tissue. Prior work has had limited success in providing a real-time solution for estimating local tissue deformation and movement; furthermore, there has been no work in estimating the accuracy of maintaining a registration --- that is, the accuracy of the registration after having been moved with the tracked tissue. In this work, we establish an image registration method between ultrasound images and endoscopic stereo-cameras using a novel registration tool; this method does not require external tracking or ultrasound probe calibration, thus providing a simple method for performing a registration. In order to maintain an image registration over time, we developed a tissue tracking framework. Its key innovation is in achieving real-time tracking of a dense tissue surface map. We use the STAR detector and Binary Robust Independent Elementary Features and compare their performance to prior tissue feature tracking methods, showing that they perform significantly faster while still managing to track the tissue at high densities. Experiments are performed on ex-vivo bovine heart, kidney, and porcine liver tissues, and initial results show that registrations can be maintained within 3 mm.
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