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Ultrasound guidance for epidural anesthesia

University of British Columbia

We propose an augmented reality system to automatically identify lumbar vertebral levels and the lamina region in ultrasound-guided epidural anesthesia. Spinal needle insertion procedures require careful placement of a needle, both to ensure effective therapy delivery and to avoid damaging sensitive tissue such as the spinal cord. An important step in such procedures is the accurate identification of the vertebral levels, which is currently performed using manual palpation with a reported success rate of only 30%. In this thesis, we propose a system using a trinocular camera which tracks an ultrasound transducer during the acquisition of a sequence of B-mode images. The system generates a panorama ultrasound image of the lumbar spine, automatically identifies the lumbar levels in the panorama image, and overlays the identified levels on a live camera view of the patient’s back. Several experiments were performed to test the accuracy of vertebral height in panorama images, the accuracy of vertebral levels identification in panorama images, the accuracy of vertebral levels identification on the skin, and the impact on accuracy with spine arching. The results from 17 subjects demonstrate the feasibility of the approach and capability of achieving an error within a clinically acceptable range for epidural anesthesia. The overlaid marks on the screen are used to assist locating needle puncture site. Then, an automated slice selection algorithm is used to guide the operator positioning a 3D transducer such that the best view of the target anatomy is visible in a predefined re-slice of the 3D ultrasound volume. This re-slice is used to observe, in real time, the trajectory of a needle attached to the 3D transducer, towards the target. The method is based on Haar-like features and AdaBoost learning algorithm. We have evaluated the method on a set of 32 volumes acquired from volunteer subjects by placing the 3D transducer on L1-L2, L2-L3, L3-L4 and L4- L5 interspinous gaps on each side of the lumbar spine. Results show that the needle insertion plane can be identified with a root mean square error of 5.4 mm, accuracy of 99.6%, and precision of 78.7%.

Text

2013-04-19

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/44306

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/