Ultrasonic characterization of the nonlinear properties of canine livers by measuring shear wave speed and axial strain with increasing portal venous pressure.
Saved in:
| Title: | Ultrasonic characterization of the nonlinear properties of canine livers by measuring shear wave speed and axial strain with increasing portal venous pressure. |
|---|---|
| Authors: | Rotemberg, Veronica1 veronica.rotemberg@duke.edu, Byram, Brett1, Palmeri, Mark1,2, Wang, Michael1, Nightingale, Kathryn1 |
| Source: | Journal of Biomechanics. 2013, Vol. 46 Issue 11, p1875-1881. 7p. |
| Subjects: | Hepatic veins, Liver diseases, Disease complications, Ultrasonic imaging, Portal vein |
| Abstract: | Elevated hepatic venous pressure is the primary source of complications in advancing liver disease. Ultrasound imaging is ideal for potential noninvasive hepatic pressure measurements as it is widely used for liver imaging. Specifically, ultrasound based stiffness measures may be useful for clinically monitoring pressure, but the mechanism by which liver stiffness increases with hepatic pressure has not been well characterized. This study is designed to elucidate the nonlinear properties of the liver during pressurization by measuring both hepatic shear wave speed (SWS) and strain with increasing pressure. Tissue deformation during hepatic pressurization was tracked in 8 canine livers using successively acquired 3-D B-mode volumes and compared with concurrently measured SWS. When portal venous pressure was increased from clinically normal (0-5 mmHg) to pressures representing highly diseased states at 20 mmHg, the liver was observed to expand with axial strain measures up to 10%. At the same time, SWS estimates were observed to increase from 1.5-2 m/s at 0-5 mmHg (baseline) to 3.25-3.5 m/s at 20 mmHg. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Biomechanics is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
Be the first to leave a comment!