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Related Experiment Video

Updated: Dec 13, 2025

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
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Real-Time Passive Acoustic Mapping Using Sparse Matrix Multiplication.

Hermes A S Kamimura, Shih-Ying Wu, Julien Grondin

    IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
    |August 4, 2020
    PubMed
    Summary
    This summary is machine-generated.

    We developed a GPU-accelerated system for real-time monitoring of cavitation during focused ultrasound (FUS) blood-brain barrier opening. This technology allows for immediate adjustments during treatment, improving safety and efficacy.

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    Area of Science:

    • Biomedical Engineering
    • Acoustics
    • Neuroscience

    Background:

    • Focused ultrasound (FUS) with microbubbles opens the blood-brain barrier, but real-time monitoring of cavitation is computationally intensive.
    • Current methods struggle with processing large datasets for immediate visualization, hindering treatment adjustments.

    Purpose of the Study:

    • To implement a GPU-accelerated system for real-time spatiotemporal monitoring of cavitation during FUS-mediated blood-brain barrier opening.
    • To assess the system's performance in silico, in vitro, and in vivo for clinical applicability.

    Main Methods:

    • Developed a GPU-accelerated sparse matrix-based beamforming and time-exposure acoustics system integrated into a neuronavigation-guided ultrasound platform.
    • Tested system performance using computational benchmarking, nonhuman primate (NHP) and human skull specimens, and in vivo NHP studies.

    Main Results:

    • Achieved real-time cavitation mapping (3.7 Hz frame rate) with acceptable quality for treatment monitoring.
    • Demonstrated stable cavitation maps for integration times >62.5 ms.
    • GPU sparse-matrix algorithm processed RF data at 1.11e-05 ms/pixel/sample.
    • Successfully localized cavitation and identified FUS phase aberrations in vivo.

    Conclusions:

    • GPU-accelerated sparse matrix computing enables real-time cavitation monitoring for FUS applications.
    • The system facilitates immediate adjustments during treatment by visualizing cavitation activity and FUS distortions.
    • This technology is crucial for safe and effective FUS-mediated procedures requiring precise control.