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arXiv 1602.08080physics.med-phstat.AP
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高强度聚焦超声治疗中基于能量的声像差估计的贝叶斯方法

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 M. Stockie

摘要:

高强度聚焦超声是一种用于治疗病变组织的非侵入性方法,其使用超声束在小体积内产生热量。应用该技术的一个常见挑战是生物介质的异质性会使超声波束散焦。在这里,我们减少的问题重新聚焦的光束的逆问题估计的声像差,由于从声辐射力成像数据的生物组织。我们解决这个逆问题,使用贝叶斯框架与分层先验和解决逆问题,使用大都市内吉布斯算法。该框架使用合成和实验数据集进行测试。我们证明了我们的方法能够使用小数据集(少至32个超声处理测试)来估计像差,这可以导致治疗过程的显着加速。此外,我们的方法与广泛的超声处理测试兼容,并可应用于其他基于能量的测量技术。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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