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首页> 外文期刊>IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control >Real-Time Passive Acoustic Mapping Using Sparse Matrix Multiplication
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Real-Time Passive Acoustic Mapping Using Sparse Matrix Multiplication

机译:使用稀疏矩阵乘法的实时无源声学映射

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Passive acoustic mapping enables the spatiotemporal monitoring of cavitation with circulating microbubbles during focused ultrasound (FUS)-mediated blood-brain barrier opening. However, the computational load for processing large data sets of cavitation maps or more complex algorithms limit the visualization in real-time for treatment monitoring and adjustment. In this study, we implemented a graphical processing unit (GPU)- accelerated sparse matrix-based beamforming and time exposure acoustics in a neuronavigation-guided ultrasound system for real-time spatiotemporal monitoring of cavitation. The system performance was tested in silico through benchmarking, in vitro using nonhuman primate (NHP) and human skull specimens, and demonstrated in vivo in NHPs. We demonstrated the stability of the cavitation map for integration times longer than 62.5 mu s. A compromise between real-time displaying and cavitation map quality obtained from beamformed RF data sets with a size of 2000x128x30 (axial pixelsxlateral pixelsxsamples) was achieved for an integration time of 1.44 mu s, which required a computational time of 0.27 s (frame rate of 3.7 Hz) and could be displayed in real-time between pulses at PRF = 2 Hz. Our benchmarking tests show that the GPU sparse- matrix algorithm processed the RF data set at a computational rate of 0.03 +/- 0.01 mu s/pixel/sample, which enables adjusting the frame rate and the integration time as needed. The neuronavigation system with real-time implementation of cavitation mapping facilitated the localization of the cavitation activity and helped to identify distortions due to FUS phase aberration. The in vivo test of themethod demonstrated the feasibility of GPU-accelerated sparse matrix computing in a close to a clinical condition, where focus distortions exemplify problems during treatment. These experimental conditions show the need for spatiotemporal monitoring of cavitation with real-time capability that enables the operator to correct or halt the sonication in case substantial aberrations are observed.
机译:被动声学映射使得在聚焦超声(FUS)介导的血脑屏障开口期间具有循环微泡的空化的时空监测。然而,用于处理大数据集的计算负载或更复杂的算法限制了实时的可视化以进行治疗监控和调整。在这项研究中,我们在神经通向导向的超声系统中实现了一种基于图形处理单元(GPU) - 加速的基于稀疏矩阵的波束形成和时间曝光声学,用于对空化的实时时空监测。系统性能通过基准测试,通过基准测试,体外使用非人类灵长类动物(NHP)和人的头骨标本,并在NHPS体内展示。我们展示了空化图的稳定性,用于长度超过62.5亩。从具有大小为2000x128x30(轴向PixelleAltalimplesXSamples的比较RF数据集之间的实时显示和空化映射质量之间的折衷以获得1.44μs的集成时间,这需要计算时间为0.27 s(帧速率3.7 Hz),可以在PRF = 2 Hz的脉冲之间实时显示。我们的基准测试表明,GPU稀疏矩阵算法以0.03 +/-0.01μmS/像素/样品的计算速率处理了RF数据集,这使得能够根据需要调整帧速率和集成时间。具有实时实施空化测绘的神经道辐射系统促进了空化活动的定位,并有助于识别由于FUS相差引起的扭曲。本发明体内试验证明了GPU加速稀疏矩阵计算在接近临床条件下的可行性,其中焦点扭曲在治疗期间举例说明问题。这些实验条件表明,需要采用实时能力的空化空化的不需要,使操作者能够在观察到大幅像差时校正或停止超声处理。

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