Volume 25, Issue 5
A Bayesian Approach for Energy-Based Estimation of Acoustic Aberrations in High Intensity Focused Ultrasound Treatment

Bamdad Hosseini, Charles Mougenot, Samuel Pichardo, Elodie Constanciel, James M. Drake & John Stockie

DOI: 10.4208/cicp.OA-2018-0007

Commun. Comput. Phys., 25 (2019), pp. 1564-1590.

Published online: 2019-01

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  • 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.

  • Keywords

Focused ultrasound, MR-ARFI, inverse problem, Bayesian, parameter estimation.

  • AMS Subject Headings

65K99, 65Z05, 62G08, 62P35

  • Copyright

COPYRIGHT: © Global Science Press

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@Article{CiCP-25-1564, 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 = {

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.

}, issn = {1991-7120}, doi = {https://doi.org/10.4208/cicp.OA-2018-0007}, url = {http://global-sci.org/intro/article_detail/cicp/12962.html} }
TY - JOUR T1 - A Bayesian Approach for Energy-Based Estimation of Acoustic Aberrations in High Intensity Focused Ultrasound Treatment JO - Communications in Computational Physics VL - 5 SP - 1564 EP - 1590 PY - 2019 DA - 2019/01 SN - 25 DO - http://doi.org/10.4208/cicp.OA-2018-0007 UR - https://global-sci.org/intro/article_detail/cicp/12962.html KW - Focused ultrasound, MR-ARFI, inverse problem, Bayesian, parameter estimation. AB -

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.

Bamdad Hosseini, Charles Mougenot, Samuel Pichardo, Elodie Constanciel, James M. Drake & John Stockie. (2020). A Bayesian Approach for Energy-Based Estimation of Acoustic Aberrations in High Intensity Focused Ultrasound Treatment. Communications in Computational Physics. 25 (5). 1564-1590. doi:10.4208/cicp.OA-2018-0007
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