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Updated: Oct 19, 2026

Imaging and Quantification of the Area of Fast-Moving Microbubbles Using a High-Speed Camera and Image Analysis
Published on: September 5, 2020
Image-domain assessment and quantitative evaluation of acoustic cavitation using singular-value decomposition and
Xinyue Gong1, Zijun Liu1, Guofeng Zhang1
1Department of Acoustics, School of Physics, Jiangsu Key Laboratory for Cardiovascular Information and Health Engineering Medicine, Nanjing University, Nanjing 210093, China.
Abstract:
Acoustic cavitation is a transient and spatially heterogeneous process that governs energy deposition and mechanical effects during high-intensity focused ultrasound (HIFU) exposure. Active B-mode imaging is attractive for monitoring because it is readily available on clinical ultrasound systems; however, therapy-imaging coupling can introduce drifting stripe artifacts that overlap with cavitation backscatter and compromise gray-level thresholding and region of interest (ROI) based quantification. Here we present an image-domain B-mode framework for cavitation-associated feature separation and spatiotemporal quantification based on singular-value decomposition (SVD) and magnitude-based spectral gating. B-mode sequences are rearranged into a spatiotemporal matrix, decomposed into singular components, and reconstructed into full-field maps of cavitation-associated image features from finite frame stacks. Highly coherent components are assigned to quasi-steady HIFU-related echoes and background scattering, whereas candidate cavitation components are selected according to their spectral-magnitude distribution, allowing stripe components observed in the present B-mode sequences to be suppressed while intermittent candidate cavitation-associated image fluctuations are retained. Experiments in egg-white polyacrylamide gels and ex vivo bovine liver showed that the proposed method suppressed stripe artifacts, improved frame-to-frame consistency, and detected cavitation onset earlier than conventional grayscale thresholding. The image-derived cavitation area increased with both acoustic pressure and pulse length and followed the trends observed in post-exposure axial damage morphology. This unsupervised method requires neither training data nor manual ROI selection, and therefore provides a practical image-based metric for retrospective visualization and quantification of cavitation-associated features during therapeutic ultrasound exposures.
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