Nonlinear elasticity imaging

Goenezen, Sevan; Oberai, Assad A.; Dord, Jean-Francois; Sink, Zac; Barbone, Paul E.

doi:10.1109/nebc.2011.5778680

Cited by 7 publications

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this work, we completely discard the low precision lateral displacement components from the minimization process. Previous elastic property reconstructions on tissue mimicking phantoms and synthetic data have demonstrated that accurate modulus reconstructions can be obtained in this manner [39], [40]. …”

Section: Methodsmentioning

confidence: 99%

Linear and Nonlinear Elastic Modulus Imaging: An Application to Breast Cancer Diagnosis

Goenezen

Dord

Sink

et al. 2012

IEEE Trans. Med. Imaging

Self Cite

117

View full text Add to dashboard Cite

We reconstruct the in vivo spatial distribution of linear and nonlinear elastic parameters in ten patients with benign (five) and malignant (five) tumors. The mechanical behavior of breast tissue is represented by a modified Veronda–Westmann model with one linear and one nonlinear elastic parameter. The spatial distribution of these elastic parameters is determined by solving an inverse problem within the region of interest (ROI). This inverse problem solution requires the knowledge of the displacement fields at small and large strains. The displacement fields are measured using a free-hand ultrasound strain imaging technique wherein, a linear array ultrasound transducer is positioned on the breast and radio frequency echo signals are recorded within the ROI while the tissue is slowly deformed with the transducer. Incremental displacement fields are determined from successive radio-frequency frames by employing cross-correlation techniques. The rectangular regions of interest were subjectively selected to obtain low noise displacement estimates and therefore were variables that ranged from 346 to 849.6 mm2. It is observed that malignant tumors stiffen at a faster rate than benign tumors and based on this criterion nine out of ten tumors were correctly classified as being either benign or malignant.

show abstract

Section: Methodsmentioning

confidence: 99%

Linear and Nonlinear Elastic Modulus Imaging: An Application to Breast Cancer Diagnosis

Goenezen

Dord

Sink

et al. 2012

IEEE Trans. Med. Imaging

Self Cite

117

View full text Add to dashboard Cite

show abstract

Spatially Weighted Objective Function to Solve the Inverse Elasticity Problem for the Elastic Modulus

Mei

Goenezen

2015

Computational Biomechanics for Medicine

View full text Add to dashboard Cite

On improving the accuracy of nonhomogeneous shear modulus identification in incompressible elasticity using the virtual fields method

Mei

Avril

2019

International Journal of Solids and Structures

View full text Add to dashboard Cite

This paper discusses an important issue about the virtual fields method when it is used to identify nonhomogeneous shear moduli of nearly incompressible solids. From simulated examples, we observed that conventional virtual fields, which assign null displacements on the entire boundary, do not perform well on nonhomogeneous and nearly incompressible solids. Thus, these conventional virtual fields should not be used for such materials. We propose two novel types of virtual fields derived from either finite element analyses performed on the same domain with homogeneous properties or computing the curl of a potential vector field. From a variety of simulated and experimental examples, we observe that the proposed virtual fields significantly improve the accuracy of the estimated shear moduli of nonhomogeneous and nearly incompressible solids. Furthermore, the sensitivity to noise of the proposed approach is moderate and the approach can handle cases with unknown boundary conditions. Therefore, based on this careful and thorough analysis, it is concluded that the proposed approaches are a significant improvement of the VFM to identify nonhomogeneous shear moduli in nearly incompressible solids. KeywordsIdentification of nonhomogeneous shear modulus distribution, virtual fields methods, incompressible elasticity, virtual fields, inverse problem.

show abstract

Nonlinear elasticity imaging

Cited by 7 publications

References 10 publications

Linear and Nonlinear Elastic Modulus Imaging: An Application to Breast Cancer Diagnosis

Linear and Nonlinear Elastic Modulus Imaging: An Application to Breast Cancer Diagnosis

Spatially Weighted Objective Function to Solve the Inverse Elasticity Problem for the Elastic Modulus

On improving the accuracy of nonhomogeneous shear modulus identification in incompressible elasticity using the virtual fields method

Contact Info

Product

Resources

About