@Article{CiCP-25-5,
author = {},
title = {A Bayesian Approach for Energy-Based Estimation of Acoustic Aberrations in High Intensity Focused Ultrasound Treatment},
journal = {Communications in Computational Physics},
year = {2019},
volume = {25},
number = {5},
pages = {1564--1590},
abstract = {<p style="text-align: justify;">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.</p>},
issn = {1991-7120},
doi = {https://doi.org/10.4208/cicp.OA-2018-0007},
url = {https://global-sci.com/article/79933/a-bayesian-approach-for-energy-based-estimation-of-acoustic-aberrations-in-high-intensity-focused-ultrasound-treatment}
}