A Bayesian approach for characterization of soft tissue viscoelasticity in acoustic radiation force imaging

2015 ◽  
Vol 32 (4) ◽  
pp. e02741 ◽  
Author(s):  
Xiaodong Zhao ◽  
Assimina A. Pelegri
Keyword(s):  
Soft Tissue ◽  
Bayesian Approach ◽  
Acoustic Radiation ◽  
Radiation Force ◽  
Acoustic Radiation Force

Direction of acoustic radiation force on spherical scatterers in soft tissue

2015 ◽  
Vol 137 (4) ◽  
pp. 2313-2313
Author(s):  
Benjamin C. Treweek ◽  
Yurii A. Ilinskii ◽  
Evgenia A. Zabolotskaya ◽  
Mark F. Hamilton
Keyword(s):  
Soft Tissue ◽  
Acoustic Radiation ◽  
Radiation Force ◽  
Acoustic Radiation Force

Viscoelastic characterization of thin tissues using acoustic radiation force and model-based inversion

2009 ◽  
Vol 54 (13) ◽  
pp. 4089-4112 ◽  
Author(s):  
Bojan B Guzina ◽  
Kairat Tuleubekov ◽  
Dalong Liu ◽  
Emad S Ebbini
Keyword(s):  
Acoustic Radiation ◽  
Radiation Force ◽  
Acoustic Radiation Force ◽  
Model Based

scholarly journals Role of Acoustic Radiation Force Impulse Elastography in the Characterization of Focal Solid Hepatic Lesions

2018 ◽  
Vol 8 ◽  
pp. 5 ◽  
Author(s):  
Harshavardhan Nagolu ◽  
Sudhakar Kattoju ◽  
Chidambaranathan Natesan ◽  
Meera Krishnakumar ◽  
Sunil Kumar
Keyword(s):  
Acoustic Radiation ◽  
Radiation Force ◽  
Acoustic Radiation Force ◽  
Acoustic Radiation Force Impulse ◽  
Benign Lesions ◽  
Metastatic Lesions ◽  
Arfi Elastography ◽  
Malignant Lesions ◽  
2D Imaging

Objective: The purpose of the study is to investigate the usefulness of acoustic radiation force impulse (ARFI) elastography in the characterization of focal solid liver lesions as benign, malignant, or metastatic using ARFI two-dimensional (2D) imaging and ARFI quantification (shear wave velocities [SWVs]). Materials and Methods: Sixty lesions were included in this study. The lesions were classified into three groups: Group I included benign lesions (n = 25), Group II included malignant lesions (n = 27), and Group III included metastatic lesions (n = 8). ARFI elastography was performed in all these patients using a Siemens ACUSON S 2000™ ultrasound machine. Stiffness and size of the lesions were assessed on ARFI 2D images in correlation with B-mode ultrasound images. SWVs were obtained in these lesions for the quantification of stiffness. Results: In ARFI 2D images, malignant lesions were predominantly stiffer and larger, while benign lesions were softer and similar in size (P < 0.05). The mean SWVs in benign, malignant, and metastatic lesions were 1.30 ± 0.35 m/s, 2.93 ± 0.75 m/s, and 2.77 ± 0.90 m/s, respectively. The area under receiver operating characteristic curve of SWV for differentiating benign from malignant lesions was 0.877, suggesting fair accuracy (95% confidence interval: 0.777–0.976); with a cutoff value of 2 m/s, showing sensitivity: 92%; specificity: 96%; positive predictive value: 96%; negative predictive value: 93% (P < 0.05). Statistically significant difference exists in SWV of benign and malignant or metastatic lesions. Conclusion: ARFI elastography with 2D imaging and quantification might be useful in the characterization of benign and malignant liver lesions.

Dynamic Analysis of Soft Tissue Viscoelasticity Under Ultrasonic Radiation Force Using FEM

2007 ◽  
Author(s):  
Megan L. Kogit ◽  
Baoxiang Shan ◽  
Assimina A. Pelegri
Keyword(s):  
Finite Element ◽  
Soft Tissue ◽  
Dynamic Analysis ◽  
Young’S Modulus ◽  
Acoustic Radiation ◽  
Young's Modulus ◽  
Radiation Force ◽  
Acoustic Radiation Force ◽  
Damping Coefficient ◽  
Response Data

We have developed a solid mechanics model of nearly incompressible, viscoelastic soft tissue for finite element analysis (FEA) in MATLAB 7.2. Newmark’s method was used to solve the finite element equations of motion for our model. The solution to our dynamic problem was validated with a transient dynamic analysis in ANSYS 10.0. We further demonstrated that our MATLAB FEA qualitatively agrees with those results observed with acoustic radiation force methods on soft tissues and tissue-mimicking materials. We showed that changes in Young’s modulus and the damping coefficient affect the displacement amplitude and phase shift of the response data in the same manner: An increase in Young’s modulus or damping coefficient decreases both the displacement amplitude and response lag. Future work on this project will involve frequency analysis on response data and studying the initial transient region to help uncouple the effects of Young’s modulus and damping coefficient on response characteristics. This will get us one step closer to being able to explicitly determine Young’s modulus and the damping coefficient from the temporal response data of acoustic radiation force methods, which is the ultimate goal of our project.

Sign in / Sign up

Export Citation Format

Share Document