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