Estimation of Dielectric Parameters from Ultrasound Waves in Quantitative Thermoacoustic Tomography

TL;DR

This paper presents a Bayesian-based method for estimating dielectric parameters, specifically electrical conductivity and permittivity, from ultrasound data in quantitative thermoacoustic tomography, validated through numerical simulations.

Contribution

It introduces a novel approach for simultaneous dielectric parameter estimation in QTAT using a Bayesian inverse problem framework based on Maxwell's equations.

Findings

Dielectric parameters can be accurately estimated with the proposed method.

Sensor geometry significantly affects estimation accuracy.

Number of electromagnetic pulses influences the quality of parameter recovery.

Abstract

Thermoacoustic tomography (TAT) is an imaging modality based on the thermoacoustic effect. In TAT, a short microwave or radio wave pulse is directed to the imaged target. The energy of the electromagnetic pulse is absorbed depending on the target's dielectric parameters, resulting in a spatially varying pressure distribution via the thermoacoustic effect. This pressure, known as the initial pressure distribution, relaxes as broadband ultrasound waves that are measured on the boundary of the target. In the inverse problem of TAT, the initial pressure is estimated from the measured ultrasound waves. TAT can further be extended to quantitative TAT (QTAT), where the aim is to estimate the dielectric parameters of the imaged target by utilizing a model for electromagnetic wave propagation. In this work, we consider the QTAT problem and propose an approach for simultaneous estimation of electrical conductivity and permittivity from the ultrasound waves. The problem is approached in the framework of Bayesian inverse problems. The forward model describing electromagnetic and acoustic wave propagation is based on Maxwell's equations and the acoustic wave equation, respectively. The approach is evaluated with numerical simulations. The results show that the dielectric parameters can be accurately estimated using the proposed approach. However, the ultrasound sensor geometry and the number of electromagnetic pulses have a significant effect on the accuracy of the estimated parameters.