A Bayesian Approach for Energy-Based Estimation of Acoustic Aberrations in High Intensity Focused Ultrasound Treatment (Communications in Computational Physics, 2019)
High intensity focused ultrasound is a non-invasive method for treatmentof 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 thebiological medium can defocus the ultrasound beam. Here we reduce the problem ofrefocusing the beam to the inverse problem of estimating the acoustic aberration due tothe biological tissue from acoustic radiative force imaging data. We solve this inverseproblem using a Bayesian framework with a hierarchical prior and solve the inverseproblem using a Metropolis-within-Gibbs algorithm. The framework is tested usingboth synthetic and experimental datasets. We demonstrate that our approach has theability 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 otherenergy-based measurement techniques.
Keywords: Focused ultrasound; MR-ARFI; inverse problem; Bayesian; parameter estimation.