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research-article

Simulating Ultrasound Tissue Deformation Using Inverse Mapping

[+] Author and Article Information
David Pepley

The Pennsylvania State University Department of Mechanical and Nuclear Engineering, 319 Leonhard Bldg., University Park, PA 16802
dfp5036@psu.edu

Sanjib D. Adhikary

Penn State Health Milton S. Hershey Medical Center Department of Anesthesiology and Perioperative Medicine, 500 University Dr., H187, Hershey, PA 17033
sadhikary1@pennstatehealth.psu.edu

Scarlett Miller

The Pennsylvania State University School of Engineering Design, Technology, and Professional Programs, 213P Hammond Bldg., University Park, PA 16802
scarlettmiller@psu.edu

Jason Z. Moore

The Pennsylvania State University Department of Mechanical and Nuclear Engineering, 318 Leonhard Bldg., University Park, PA 16802
jzm14@psu.edu

1Corresponding author.

ASME doi:10.1115/1.4042809 History: Received September 27, 2018; Revised January 31, 2019

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 due to 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 needle-tissue 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.

Copyright (c) 2019 by ASME
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