IEEE Trans Ultrason Ferroelectr Freq Control. 2021 Jan;68(1):164-177. doi: 10.1109/TUFFC.2020.3001848. Epub 2020 Dec 23.
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 [Formula: see text]. A compromise between real-time displaying and cavitation map quality obtained from beamformed RF data sets with a size of 2000 ×128 ×30 (axial [Formula: see text]) was achieved for an integration time of [Formula: see text], 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 [Formula: see text]/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.
被动声学映射可实现微泡声空化的时空监测，用于聚焦超声（FUS）介导的血脑屏障开放。然而，处理大量声空化图数据集或更复杂算法的计算负荷限制了治疗监测和调整的实时可视化。在这项研究中，我们在神经导航引导超声系统中实现了基于图形处理单元（GPU）加速稀疏矩阵的波束形成和时变声学，用于实时声空化时空监测。通过基准测试、非人类灵长类动物（NHP）和人类颅骨标本的体外测试以及 NHP 的体内演示，测试了系统性能。我们证明了在超过 62.5 [Formula: see text] 的积分时间下，声空化图的稳定性。通过对大小为 2000×128×30（轴向 [Formula: see text]）的波束形成 RF 数据集进行实时显示和空化图质量之间的折衷处理，实现了 [Formula: see text] 的积分时间，这需要 0.27 s 的计算时间（帧率为 3.7 Hz），并且可以在 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 [Formula: see text]/像素/样本的计算速率处理 RF 数据集，这使得能够根据需要调整帧率和积分时间。具有实时声空化映射功能的神经导航系统有助于对空化活动进行定位，并有助于识别由于 FUS 相位像差引起的失真。该方法的体内测试证明了 GPU 加速稀疏矩阵计算在接近临床条件下的可行性，在这种条件下，焦点失真说明了治疗过程中的问题。这些实验条件表明需要实时监测声空化，以便操作员在观察到实质性失真时能够纠正或停止超声处理。
