A Bayesian approach for energy-based estimation of acoustic aberrations in high intensity focused ultrasound treatment

TL;DR

This paper presents a Bayesian hierarchical method to estimate acoustic aberrations in high intensity focused ultrasound, improving beam refocusing with small datasets and broad applicability.

Contribution

Introduces a Bayesian inverse problem framework with a hierarchical prior and Metropolis-within-Gibbs sampling for estimating tissue-induced aberrations in ultrasound therapy.

Findings

Accurately estimates aberrations with as few as 32 tests.

Compatible with various energy-based measurement techniques.

Demonstrates effectiveness on synthetic and experimental data.

Abstract

High intensity focused ultrasound is a non-invasive method for treatment of diseased tissue that uses a beam of ultrasound to generate heat within a small volume. A common challenge in application of this technique is that heterogeneity of the biological medium can defocus the ultrasound beam. Here we reduce the problem of refocusing the beam to the inverse problem of estimating the acoustic aberration due to the biological tissue from acoustic radiative force imaging data. We solve this inverse problem using a Bayesian framework with a hierarchical prior and solve the inverse problem using a Metropolis-within-Gibbs algorithm. The framework is tested using both synthetic and experimental datasets. We demonstrate that our approach has the ability to estimate the aberrations using small datasets, as little as 32 sonication tests, which can lead to significant speedup in the treatment process. Furthermore, our approach is compatible with a wide range of sonication tests and can be applied to other energy-based measurement techniques.