Title
Viscoelastic response ultrasound: Methods, validation, and in vivo clinical applications of a new approach to viscoelastic property assessment
Date Issued
07 September 2020
Access level
metadata only access
Resource Type
conference paper
Author(s)
University of North Carolina
Publisher(s)
IEEE Computer Society
Abstract
Viscoelastic Response (VisR) ultrasound is a noninvasive method for interrogating the viscoelastic properties of tissue by observing, in the region of excitation, tissue displacement in response to successive acoustic radiation force impulses. Recent VisR technology advancements now enable estimation of mechanical anisotropy and texture as well as, via machine learning approaches, quantitative evaluation of elastic and viscous moduli (as in Quantitative VisR (QVisR)). The diagnostic relevance of VisR outcome measures have been demonstrated for monitoring renal transplant status, differentiating malignant from benign breast masses, and delineating dystrophic muscle degeneration. For the latter two applications, method and pilot clinical results are reviewed herein. VisR-derived relative elasticity (RE) and relative viscosity (RV) mechanical anisotropy measures differentiated malignant from benign breast masses in women with sensitivities of 0.94 and specificities of 1.00, with accuracies of 0.96. In dystrophic muscle, QVisR-derived longitudinal shear elastic modulus in rectus femoris, vastus lateralus, sartorius, and gastrocnemius muscles statistically different between dystrophic and control for ages less than six years (Wilcoxon, p<0.05). Overall, the body of work describing VisR methods and their applications suggests that VisR is a pertinent tool for clinical tissue viscoelasticity assessment.
Volume
2020-September
Language
English
OCDE Knowledge area
Radiología, Medicina nuclear, Imágenes médicas
Ingeniería médica
Subjects
Scopus EID
2-s2.0-85097873831
ISSN of the container
19485719
ISBN of the container
978-172815448-0
Conference
IEEE International Ultrasonics Symposium, IUS
Sponsor(s)
ACKNOWLEDGMENT This work was supported by Siemens Healthineers, Ultrasound Division; the National Institutes of Health under award numbers R01HL092944, R01NS074057, and R01DK107740; Research Computing at UNC Chapel Hill; and the Lineberger Comprehensive Cancer Center (LCCC) at UNC Chapel Hill.
Sources of information:
Directorio de Producción Científica
Scopus