论文标题

组织疗法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

论文作者

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

论文摘要

已经使用了多种方法来模拟单个孤立的气泡的动力学,该动力学由微秒长度高振幅超声脉冲(例如,组织疗法脉冲)成核。直到最近,在特征良好的驾驶压力下缺乏单泡实验半径与气泡动力学的时间数据具有有限的模型验证工作。这项研究使用了半径与时间测量的时间测量,用于水中的单个球形组织颗粒核的气泡[Wilson等,Phys。 Rev. E,2019,99,043103]用于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。提出了一种直接从实验半径与时间和空化阈值数据来推断组织摄氏波形的分析表示的策略。我们比较了用于$ 88 $实验数据集的每个模型获得的计算验证度量的分布。在本研究中考虑的可压缩性和热效应的建模方法中,有很小的区别($ <1 \%$)。这些结果表明,我们提出的推断波形的策略,结合简单的模型最小化参数不确定性和计算资源需求准确地代表了组织肌肉中的单泡动力学,包括AT和接近最大气泡半径。讨论了剩余的参数和基于模型的不确定性来源。

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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