Published in

American Society of Mechanical Engineers, Journal of Computational and Nonlinear Dynamics, 10(14), 2019

DOI: 10.1115/1.4042809

Links

Tools

Export citation

Search in Google Scholar

Simulating Ultrasound Tissue Deformation Using Inverse Mapping

Journal article published in 2019 by David F. Pepley, Sanjib D. Adhikary ORCID, Scarlett R. Miller, Jason Z. Moore
This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

Full text: Unavailable

Green circle
Preprint: archiving allowed
Green circle
Postprint: archiving allowed
Red circle
Published version: archiving forbidden
Data provided by SHERPA/RoMEO

Abstract

Abstract Ultrasound guidance is used for a variety of surgical needle insertion procedures, but there is currently no standard for the teaching of ultrasound skills. Recently, computer ultrasound simulation has been introduced as an alternative teaching method to traditional manikin and cadaver training because of its ability to provide diverse scenario training, quantitative feedback, and objective assessment. Current computer ultrasound training simulation is limited in its ability to image tissue deformation caused by needle insertions, even though tissue deformation identification is a critical skill in performing an ultrasound-guided needle insertion. To fill this need for improved simulation, a novel method of simulating ultrasound tissue–needle deformation is proposed and evaluated. First, a cadaver study is conducted to obtain ultrasound video of a peripheral nerve block. Then, optical flow analysis is conducted on this video to characterize the tissue movement due to the needle insertion. Tissue movement is characterized into three zones of motion: tissue near the needle being pulled, and zones above and below the needle where the tissue rolls. The rolling zones were centered 1.34 mm above and below the needle and 4.53 mm behind the needle. Using this characterization, a vector field is generated mimicking these zones. This vector field is then applied to an ultrasound image using inverse mapping to simulate tissue movement. The resulting simulation can be processed at 3.1 frames per second. This methodology can be applied through future optimized graphical processing to allow for accurate real time needle tissue simulation.