By Topic

Robust estimation of time-of-flight shear wave speed using a Radon sum transformation

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Rouze, N.C. ; Dept. of Biomed. Eng., Duke Univ., Durham, NC, USA ; Wang, M.H. ; Palmeri, M.L. ; Nightingale, K.R.

Time-of-flight methods allow quantitative measurement of shear wave speed (SWS) from ultrasonically tracked displacements following impulsive excitation in tissue. However, application of these methods to in vivo data is challenging due to the presence of gross outlier data resulting from sources such as physiological motion or spatial inhomogeneities. This paper describes a new method for estimating SWS by considering a solution space of trajectories and evaluating each trajectory using a metric that characterizes wave motion along the entire trajectory. The metric used here is found by summing displacement data along the trajectory as in the calculation of projection data in the Radon transformation. The algorithm is evaluated using data acquired in calibrated phantoms and in vivo human liver. Results are compared to SWS estimates using a random sample consensus (RANSAC) algorithm described by Wang, et al. Good agreement is found between the Radon sum and RANSAC SWS estimates with a correlation coefficient of greater than 0.99 for phantom data and 0.91 for in vivo liver data. The Radon sum transformation is suitable for use in situations requiring realtime feedback and is comparably robust to the RANSAC algorithm with respect to outlier data. I.

Published in:

Ultrasonics Symposium (IUS), 2010 IEEE

Date of Conference:

11-14 Oct. 2010