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arXiv 2006.10171physics.flu-dynphysics.bio-ph
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组织破坏和高振幅超声中的单泡动力学:建模和验证

Single-bubble dynamics in histotripsy and high-amplitude ultrasound: Modeling and validation

Lauren Mancia, Mauro Rodriguez, Jonathan Sukovich, Zhen Xu, Eric Johnsen

摘要:

已经使用了多种方法来模拟由微秒长度的高振幅超声脉冲(例如,组织破坏脉冲)。直到最近,缺乏单一的气泡实验半径与时间数据的气泡动力学下一个良好的特点驱动压力有限的模型验证工作。该研究使用水中单个球形组织三核气泡的半径与时间的测量[Wilson等人,Phys. Rev. E,2019,99,043103]定量比较和验证各种气泡动力学建模方法,包括可压缩和不可压缩模型以及不同的热模型。直接从实验半径与时间和空化阈值数据推断组织碎石波形的分析表示的策略。我们比较了每个模型应用于$88$实验数据集的计算验证度量的分布。在本研究中考虑的压缩性和热效应的建模方法之间存在最小的区别($< 1\%$)。这些结果表明,我们提出的推断波形的策略,结合简单的模型,最大限度地减少参数的不确定性和计算资源的需求准确地代表单泡动力学在histotripsy,包括在和接近最大气泡半径。其余的来源的参数和基于模型的不确定性进行了讨论。

英文摘要:

A variety of approaches have been used to model the dynamics of a single, isolated bubble nucleated by a microsecond length high-amplitude ultrasound pulse (e.g., a histotripsy pulse). Until recently, the lack of single--bubble experimental radius vs. time data for bubble dynamics under a well-characterized driving pressure has limited model validation efforts. This study uses radius vs. time measurements of single, spherical histotripsy-nucleated bubbles in water [Wilson et al., Phys. Rev. E, 2019, 99, 043103] to quantitatively compare and validate a variety of bubble dynamics modeling approaches, including compressible and incompressible models as well as different thermal models. A strategy for inferring an analytic representation of histotripsy waveforms directly from experimental radius vs. time and cavitation threshold data is presented. We compare distributions of a calculated validation metric obtained for each model applied to $88$ experimental data sets. There is minimal distinction ($< 1\%$) among the modeling approaches for compressibility and thermal effects considered in this study. These results suggest that our proposed strategy to infer the waveform, combined with simple models minimizing parametric uncertainty and computational resource demands accurately represent single-bubble dynamics in histotripsy, including at and near the maximum bubble radius. Remaining sources of parametric and model-based uncertainty are discussed.

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