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用于脉冲高强度聚焦超声治疗中瞬态空化气泡成像的动态模态分解

Dynamic Mode Decomposition for Transient Cavitation Bubbles Imaging in Pulsed High Intensity Focused Ultrasound Therapy.

作者信息

Song Minho, Sapozhnikov Oleg A, Khokhlova Vera A, Khokhlova Tatiana D

机构信息

Department of Mechanical Engineering, University of Washington, Seattle, WA 98195 USA.

Center for Industrial and Medical Ultrasound, Applied Physics Laboratory, University of Washington, Seattle, WA 98195 USA.

出版信息

bioRxiv. 2024 Mar 1:2024.02.26.582222. doi: 10.1101/2024.02.26.582222.

Abstract

Pulsed high-intensity focused ultrasound (pHIFU) can induce sparse inertial cavitation without the introduction of exogenous contrast agents, promoting mild mechanical disruption in targeted tissue. Because the bubbles are small and rapidly dissolve after each HIFU pulse, mapping transient bubbles and obtaining real-time quantitative metrics correlated to tissue damage are challenging. Prior work introduced Bubble Doppler, an ultrafast power Doppler imaging method as a sensitive means to map cavitation bubbles. The main limitation of that method was its reliance on conventional wall filters used in Doppler imaging and optimized for imaging blood flow rather than transient scatterers. This study explores Bubble Doppler enhancement using dynamic mode decomposition (DMD) of a matrix created from a Doppler ensemble for mapping and extracting the characteristics of transient cavitation bubbles. DMD was first tested with a numerical dataset mimicking the spatiotemporal characteristics of backscattered signal from tissue and bubbles. The performance of DMD filter was compared to other widely used Doppler wall filters - singular value decomposition (SVD) and infinite impulse response (IIR) highpass filter. DMD was then applied to an tissue dataset where each HIFU pulse was immediately followed by a plane wave Doppler ensemble. DMD outperformed SVD and IIR high pass filter and provided physically interpretable images of the modes associated with bubbles and their corresponding temporal decay rates. These DMD modes can be trackable over the duration of pHIFU treatment using k-means clustering method, resulting in quantitative indicators of treatment progression.

摘要

脉冲高强度聚焦超声(pHIFU)无需引入外源性造影剂即可诱导稀疏的惯性空化,从而促进靶向组织的轻度机械破坏。由于气泡较小且在每个HIFU脉冲后迅速溶解,因此绘制瞬态气泡并获得与组织损伤相关的实时定量指标具有挑战性。先前的研究引入了气泡多普勒,这是一种超快功率多普勒成像方法,作为绘制空化气泡的敏感手段。该方法的主要局限性在于它依赖于多普勒成像中使用的传统壁滤波器,这些滤波器是为血流成像而非瞬态散射体成像而优化的。本研究探索了使用由多普勒总体创建的矩阵的动态模式分解(DMD)来增强气泡多普勒,以绘制和提取瞬态空化气泡的特征。首先使用模拟来自组织和气泡的反向散射信号的时空特征的数值数据集对DMD进行测试。将DMD滤波器的性能与其他广泛使用的多普勒壁滤波器——奇异值分解(SVD)和无限脉冲响应(IIR)高通滤波器进行比较。然后将DMD应用于组织数据集,其中每个HIFU脉冲之后紧接着是平面波多普勒总体。DMD的性能优于SVD和IIR高通滤波器,并提供了与气泡相关的模式及其相应时间衰减率的可物理解释的图像。使用k均值聚类方法可以在pHIFU治疗期间跟踪这些DMD模式,从而得出治疗进展的定量指标。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a855/10925276/445b01c802e4/nihpp-2024.02.26.582222v1-f0002.jpg

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