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A Bayesian Approach for Energy-Based Estimation of Acoustic Aberrations in High Intensity Focused Ultrasound Treatment

Hosseini, Bamdad and Mougenot, Charles and Pichardo, Samuel and Constanciel, Elodie and Drake, James M. and Stockie, John (2019) A Bayesian Approach for Energy-Based Estimation of Acoustic Aberrations in High Intensity Focused Ultrasound Treatment. Communications in Computational Physics, 25 (5). pp. 1564-1590. ISSN 1815-2406. https://resolver.caltech.edu/CaltechAUTHORS:20190201-111654563

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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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.4208/cicp.oa-2018-0007DOIArticle
https://arxiv.org/abs/1602.08080arXivDiscussion Paper
Additional Information:© 2019 Global Science Press. Received 10 January 2018; Accepted (in revised version) 16 June 2018. The authors would like to thank Profs. Nilima Nigam and Chris Budd for fruitful discussions. BH and CM are thankful to the Fields Institute and the organizers of the Fields-Mprime Industrial Problem Solving Workshop during the August of 2014, where their collaboration was initiated. Finally, this work was supported in part by the Natural Sciences and Engineering Research Council of Canada, the Brain Canada Multi-Investigator Research Initiative and the Focused Ultrasound Foundation.
Funders:
Funding AgencyGrant Number
Natural Sciences and Engineering Research Council of Canada (NSERC)UNSPECIFIED
Brain CanadaUNSPECIFIED
Focused Ultrasound FoundationUNSPECIFIED
Subject Keywords:Focused ultrasound, MR-ARFI, inverse problem, Bayesian, parameter estimation
Issue or Number:5
Classification Code:AMS Subject Headings 65K99, 65Z05, 62G08, 62P35
Record Number:CaltechAUTHORS:20190201-111654563
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190201-111654563
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:92567
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:01 Feb 2019 20:56
Last Modified:03 Oct 2019 20:46

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