Geometric Ultrasound Localization Microscopy

Christopher Hahne, Raphael Sznitman
6/27/2023
cs.CVcs.LG

No official code found. Best implementation: hahnec/gulm (7 stars, last commit 2024-07-23).

Abstract

Contrast-Enhanced Ultra-Sound (CEUS) has become a viable method for non-invasive, dynamic visualization in medical diagnostics, yet Ultrasound Localization Microscopy (ULM) has enabled a revolutionary breakthrough by offering ten times higher resolution. To date, Delay-And-Sum (DAS) beamformers are used to render ULM frames, ultimately determining the image resolution capability. To take full advantage of ULM, this study questions whether beamforming is the most effective processing step for ULM, suggesting an alternative approach that relies solely on Time-Difference-of-Arrival (TDoA) information. To this end, a novel geometric framework for micro bubble localization via ellipse intersections is proposed to overcome existing beamforming limitations. We present a benchmark comparison based on a public dataset for which our geometric ULM outperforms existing baseline methods in terms of accuracy and robustness while only utilizing a portion of the available transducer data.

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Code Implementations(1)

hahnec/gulmCommunity60%

Geometric Ultrasound Localization Microscopy (MICCAI 23): Ellipse Intersections from Time-Difference-of-Arrival signals

71Shell, PythonJan 25, 20232 years ago
ceuscontrast-enhancementellipsegeometricimaging+12 more

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Cite this paper

@article{hahne2023geometric,
  title  = {Geometric Ultrasound Localization Microscopy},
  author = {Christopher Hahne and Raphael Sznitman},
  year   = {2023},
  eprint = {2306.15548},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2306.15548v3}
}

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