An inverse problem approach for elasticity imaging through vibroacoustics.
A methodology for estimating the spatial distribution of elastic moduli using the steady-state dynamic response of solids immersed in fluids is presented. The technique relies on the ensuing acoustic field from a remotely excited solid to inversely estimate the spatial distribution of Young's modulus of biological structures (e.g., breast tissue). This work proposes the use of Gaussian radial basis functions (GRBF) to represent the spatial variation of elastic moduli. GRBF are shown to possess the advantage of representing smooth functions with quasi-compact support and can efficiently represent elastic moduli distributions such as those that occur in soft biological tissue in the presence of unhealthy tissue (e.g., tumors and calcifications). The direct problem consists of a coupled acoustic-structure interaction boundary-value problem solved in the frequency domain using the finite element method. The inverse problem is cast as an optimization problem in which the error functional is defined as a measure of discrepancy between an experimentally measured response and a finite element representation of the system. Nongradient based optimization algorithms are used to solve the resulting optimization problem. The feasibility of the proposed approach is demonstrated through a series of simulations and an experiment. For comparison purposes, the surface velocity response was also used for the inverse characterization as the measured response in place of the acoustic pressure.
Duke Scholars
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Vibration
- Sensitivity and Specificity
- Reproducibility of Results
- Nuclear Medicine & Medical Imaging
- Normal Distribution
- Models, Statistical
- Models, Biological
- Image Interpretation, Computer-Assisted
- Image Enhancement
- Humans
Citation
Published In
DOI
EISSN
ISSN
Publication Date
Volume
Issue
Start / End Page
Related Subject Headings
- Vibration
- Sensitivity and Specificity
- Reproducibility of Results
- Nuclear Medicine & Medical Imaging
- Normal Distribution
- Models, Statistical
- Models, Biological
- Image Interpretation, Computer-Assisted
- Image Enhancement
- Humans