WeChat Mini Program
Old Version Features

Real-Time Passive Acoustic Mapping Using Sparse Matrix Multiplication

IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control(2020)

Columbia Univ

Cited 17|Views29
Abstract
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 μs. A compromise between real-time displaying and cavitation map quality obtained from beamformed RF data sets with a size of 2000×128×30 (axial pixels × lateral pixels × samples) was achieved for an integration time of 1.44 μs, 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 μs/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 the method 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.
More
Translated text
Key words
Drug delivery,graphical processing unit (GPU)-acceleration,nonhuman primate (NHP),passive acoustic mapping (PAM),sparse matrix,ultrasound-mediated blood-brain barrier (BBB) opening
PDF
Bibtex
收藏
AI Read Science
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper

要点】:本研究通过使用GPU加速的稀疏矩阵乘法,实现了实时被动声学映射,以监测聚焦超声治疗过程中微泡空化现象,提高了治疗监控和调整的效率。

方法】:研究采用GPU加速的稀疏矩阵算法进行束形成和时间曝光声学处理,集成于神经元导航指导的超声系统。

实验】:通过在硅中进行基准测试、使用非人类灵长类和人类头骨样本进行体外实验,并在非人类灵长类中进行体内演示。实验结果显示,在超过62.5微秒的积分时间内,空化图的稳定性得以验证。对于大小为2000x128x30的束形成射频数据集,通过1.44微秒的积分时间,实现了实时显示与空化图质量之间的平衡,计算时间为0.27秒(帧率为3.7赫兹),可以在脉冲间以2赫兹的PRF实时显示。基准测试表明,GPU稀疏矩阵算法处理射频数据集的计算速率为0.03±0.01微秒/像素/样本。