Comparison study of passive acoustic mapping and high-speed photography for monitoring in situ cavitation bubbles

Real-Time Passive Acoustic Mapping With Enhanced Spatial Resolution in Neuronavigation-Guided Focused Ultrasound for Blood-Brain Barrier Opening

OBJECTIVE

Passive acoustic mapping (PAM) provides the spatial information of acoustic energy emitted from microbubbles during focused ultrasound (FUS), which can be used for safety and efficacy monitoring of blood-brain barrier (BBB) opening. In our previous work with a neuronavigation-guided FUS system, only part of the cavitation signal could be monitored in real time due to the computational burden although full-burst analysis is required to detect transient and stochastic cavitation activity. In addition, the spatial resolution of PAM can be limited for a small-aperture receiving array transducer. For full-burst real-time PAM with enhanced resolution, we developed a parallel processing scheme for coherence-factor-based PAM (CF-PAM) and implemented it onto the neuronavigation-guided FUS system using a co-axial phased-array imaging transducer.

METHODS

Simulation and in-vitro human skull studies were conducted for the performance evaluation of the proposed method in terms of spatial resolution and processing speed. We also carried out real-time cavitation mapping during BBB opening in non-human primates (NHPs).

RESULTS

CF-PAM with the proposed processing scheme provided better resolution than that of traditional time-exposure-acoustics PAM with a higher processing speed than that of eigenspace-based robust Capon beamformer, which facilitated the full-burst PAM with the integration time of 10 ms at a rate of 2 Hz. In vivo feasibility of PAM with the co-axial imaging transducer was also demonstrated in two NHPs, showing the advantages of using real-time B-mode and full-burst PAM for accurate targeting and safe treatment monitoring.

SIGNIFICANCE

This full-burst PAM with enhanced resolution will facilitate the clinical translation of online cavitation monitoring for safe and efficient BBB opening.

Passive Cavitation Imaging Artifact Reduction Using Data-Adaptive Spatial Filtering

Passive cavitation imaging (PCI) with a clinical diagnostic array results in poor axial localization of bubble activity due to the size of the point spread function (PSF). The objective of this study was to determine if data-adaptive spatial filtering improved PCI beamforming performance relative to standard frequency-domain delay, sum, and integrate (DSI) or robust Capon beamforming (RCB). The overall goal was to improve source localization and image quality without sacrificing computation time. Spatial filtering was achieved by applying a pixel-based mask to DSI- or RCB-beamformed images. The masks were derived from DSI, RCB, or phase or amplitude coherence factors (ACFs) using both receiver operating characteristic (ROC) and precision-recall (PR) curve analyses. Spatially filtered passive cavitation images were formed from cavitation emissions based on two simulated sources densities and four source distribution patterns mimicking cavitation emissions induced by an EkoSonic catheter. Beamforming performance was assessed via binary classifier metrics. The difference in sensitivity, specificity, and area under the ROC curve (AUROC) differed by no more than 11% across all algorithms for both source densities and all source patterns. The computational time required for each of the three spatially filtered DSIs was two orders of magnitude less than that required for time-domain RCB and thus this data-adaptive spatial filtering strategy for PCI beamforming is preferable given the similar binary classification performance.

Real-Time Passive Acoustic Mapping Using Sparse Matrix Multiplication

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.