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Studying Cavitation Enhanced Therapy

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Published on: April 9, 2021

Passive cavitation mapping with temporal sparsity constraint.

Miklós Gyöngy¹, Christian M Coviello

¹Faculty of Information Engineering, Pázmány Péter Catholic University, Budapest 1083, Hungary. gyongy.miklos@itk.ppke.hu

The Journal of the Acoustical Society of America

|November 18, 2011

Summary

This summary is machine-generated.

Sparse deconvolution techniques like matching pursuit and basis pursuit improve cavitation mapping resolution. These methods recover lost frequency data, enhancing the passive mapping of cavitation activity for better source identification.

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Area of Science:

Acoustics
Signal Processing
Medical Imaging

Background:

Spatial resolution of cavitation maps is limited by receiver bandlimits.
Passive cavitation mapping relies on recorded emissions.
Deconvolution can recover attenuated and out-of-band frequencies.

Purpose of the Study:

To apply sparse deconvolution techniques to improve passive cavitation mapping.
To compare the effectiveness of matching pursuit and basis pursuit for cavitation mapping.

Main Methods:

Simulated and experimental cavitation recordings were used.
Two sparse deconvolution algorithms, matching pursuit and basis pursuit, were applied.
Deconvolved data was then beamformed to create passive cavitation maps.

Main Results:

Matching pursuit reduced the point spread function diameter by nearly a third, but increased susceptibility to interference.
Basis pursuit increased the point spread function diameter by ~20% but improved resolution of multiple sources in experimental data.

Conclusions:

Sparse deconvolution enhances passive cavitation mapping resolution.
Matching pursuit and basis pursuit offer different trade-offs for improving cavitation mapping.
Basis pursuit shows promise for resolving multiple cavitation sources.