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Proceedings Paper

Lesion contrast and detection using sonoelastographic shear velocity imaging: preliminary results
Author(s): Kenneth Hoyt; Kevin J. Parker
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Paper Abstract

This paper assesses lesion contrast and detection using sonoelastographic shear velocity imaging. Shear wave interference patterns, termed crawling waves, for a two phase medium were simulated assuming plane wave conditions. Shear velocity estimates were computed using a spatial autocorrelation algorithm that operates in the direction of shear wave propagation for a given kernel size. Contrast was determined by analyzing shear velocity estimate transition between mediums. Experimental results were obtained using heterogeneous phantoms with spherical inclusions (5 or 10 mm in diameter) characterized by elevated shear velocities. Two vibration sources were applied to opposing phantom edges and scanned (orthogonal to shear wave propagation) with an ultrasound scanner equipped for sonoelastography. Demodulated data was saved and transferred to an external computer for processing shear velocity images. Simulation results demonstrate shear velocity transition between contrasting mediums is governed by both estimator kernel size and source vibration frequency. Experimental results from phantoms further indicates that decreasing estimator kernel size produces corresponding decrease in shear velocity estimate transition between background and inclusion material albeit with an increase in estimator noise. Overall, results demonstrate the ability to generate high contrast shear velocity images using sonoelastographic techniques and detect millimeter-sized lesions.

Paper Details

Date Published: 12 March 2007
PDF: 8 pages
Proc. SPIE 6513, Medical Imaging 2007: Ultrasonic Imaging and Signal Processing, 65130L (12 March 2007); doi: 10.1117/12.709480
Show Author Affiliations
Kenneth Hoyt, Univ. of Rochester (United States)
Kevin J. Parker, Univ. of Rochester (United States)


Published in SPIE Proceedings Vol. 6513:
Medical Imaging 2007: Ultrasonic Imaging and Signal Processing
Stanislav Y. Emelianov; Stephen A. McAleavey, Editor(s)

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