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Multi-objective optimization of virtual source distributions for ultrafast diverging wave imaging.

Zahraa Alzein1, Marco Crocco2, Daniele D Caviglia3

  • 1Department of Electrical, Electronic, Telecommunications Engineering, and Naval Architecture (DITEN) University of Genoa, Genoa, 16145, Italy; Esaote S.p.A, Genova, 1645, Liguria, Italy.

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Summary

Optimizing virtual source (VS) placement in diverging wave imaging (DWI) with a genetic algorithm significantly improves image quality. This method enhances resolution and contrast, overcoming field-of-view limitations in ultrafast ultrasound.

Keywords:
Diverging waveGenetic algorithmsMulti-objective optimizationSpatial distributionVirtual sources

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Area of Science:

  • Ultrasound imaging
  • Medical physics
  • Computational imaging

Background:

  • Diverging wave imaging (DWI) uses virtual sources (VSs) to overcome field-of-view (FOV) limitations in ultrafast imaging.
  • Current VS placement strategies use deterministic distributions, leading to trade-offs between image resolution and contrast.

Purpose of the Study:

  • To optimize the spatial distribution of virtual sources (VSs) for convex arrays in diverging wave imaging (DWI).
  • To enhance image quality metrics including resolution and contrast ratio.
  • To investigate performance trade-offs with reduced transmission events.

Main Methods:

  • Developed a multi-objective genetic algorithm to optimize VS spatial distributions.
  • Incorporated a compound mask weighting strategy for enhanced beam coherence and artifact reduction.
  • Evaluated performance using point spread function (PSF) metrics (FWHM, PSL) and image quality metrics (CR, SNR) in simulations and experiments.

Main Results:

  • Optimized VS distributions achieved up to a 50% reduction in Full Width at Half Maximum (FWHM).
  • Demonstrated up to a 60% improvement in contrast ratio (CR) compared to deterministic methods.
  • Maintained performance gains even with a reduced number of transmission events.

Conclusions:

  • The proposed multi-objective genetic algorithm effectively optimizes VS placement for improved DWI performance.
  • This approach offers a significant advancement over deterministic VS distribution methods for convex arrays.
  • The method provides a framework for balancing image quality and transmission efficiency in ultrafast ultrasound.