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.

Automated summary

Data-adaptive spatial filtering improves the performance of passive cavitation imaging (PCI) beamforming, enhancing source localization and image quality while reducing computation time.