| Summary: | The integration of ultrasound into robotic laparoscopic surgery can provide a surgeon with navigational guidance that could decrease operating times and increase surgeon confidence during complicated procedures such as partial nephrectomy. CT scans are taken for diagnosis before surgery and have a wide field-of-view and high resolution but do not provide real-time information for the surgeon during surgery. Ultrasound is an inexpensive, portable, non-invasive imaging modality, which has the potential to provide the surgeon with real-time information. With accurate registration between CT and ultrasound, a surgeon can be provided with a wide field-of-view of the patients underlying anatomy, that cannot be seen through the laparoscope.
Organ motion within the abdomen between the time of diagnostic scanning and intra-operative imaging can affect the accuracy of image registration. CT scans of the patients’ kidneys in both the supine (diagnostic) and flank (surgical) positions were registered to determine the extent of kidney motion. The center of mass was observed to move between 10 and 75 mm resulting in a recommendation that diagnostic CT scans be performed with the patient in the potential surgical position when image registration will be performed.
This thesis presents the design of a new intra-abdominal ultrasound transducer, which can be controlled directly by the operating surgeon throughout the duration of the procedure. Initial use of the transducer is targeted for robotic laparoscopic surgery, where the operating surgeon must rely on a patient-side assistant. Multiple tracking methods have been integrated into the transducer to allow 3D ultrasound volumes to be constructed from a set of 2D slices. These methods include tracking using electromagnetic sensors, optical markers and robotic kinematics.
The vessels of the kidney serve as important landmarks during the surgical procedure and can also be used as features for CT to ultrasound registration. A registration method using automatic ultrasound vessel segmentation is proposed and tested in a phantom and human model. The root mean square error in the phantom was calculated to be 3.2 mm, which is comparable to other reported registration errors, while the error in the registration using the human model was 7.5 mm.
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