An Inverse Problem Approach for Elasticity Imaging through Vibroacoustics

Miguel Alejandro Aguilo, Wilkins Aquino, John C. Brigham, Mostafa Fatemi

IEEE Transactions on Medical Imaging · 2010 · 56 citations · 35 references

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TL;DR

The study proposes a method to estimate the spatial distribution of elastic moduli in solids immersed in fluids by modeling the steady‑state dynamic response and representing modulus variation with Gaussian radial basis functions. The method uses the acoustic field from a remotely excited solid, solves a coupled acoustic‑structure interaction problem in the frequency domain with finite element analysis, and recovers the modulus distribution by minimizing the discrepancy between measured and simulated responses using nongradient optimization. Gaussian radial basis functions efficiently capture smooth modulus distributions, including tumors, and the approach is validated through simulations and an experimental demonstration.

Abstract

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.

References

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