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Related Experiment Video

Updated: May 13, 2025

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Towards improving breast cancer detection through multi-modal image generation.

Sahar Almahfouz Nasser1, Ashutosh Sharma2, Anmol Saraf1

  • 1Electrical Engineering, Indian Institute of Technology Bombay, POWAI, Mumbai, 400076, Maharashtra, India.

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|April 22, 2025
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Summary

This study enhances ultrasound (US) imaging by using wave interference patterns to generate mammogram-quality images. This innovation improves diagnostic detail in real-time, aiding breast cancer detection.

Keywords:
Domain adaptationGANGenerativeMammogram reconstructionSpeckle noiseStrideUltrasound simulation

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Area of Science:

  • Medical Imaging
  • Biomedical Engineering
  • Computational Imaging

Background:

  • Ultrasound (US) imaging offers portability and real-time capabilities, making it suitable for resource-limited settings and intra-operative use.
  • However, US imaging suffers from lower spatial resolution and more artifacts compared to mammography.
  • Existing US enhancement techniques often treat artifacts as noise, discarding valuable information.

Purpose of the Study:

  • To enhance ultrasound images to achieve mammogram-like quality in real-time.
  • To leverage artifacts in US images as valuable indicators of tissue characteristics.
  • To provide surgeons with improved diagnostic capabilities through enhanced US imaging.

Main Methods:

  • Utilized Stride software to solve forward wave-equations, generating US images from mammograms.
  • Added high-frequency components to create realistic US images.
  • Trained a generative adversarial network (GAN) to convert US images into mammogram-quality images.

Main Results:

  • The generative process produced realistic US images with enhanced details.
  • The GAN successfully generated mammogram-quality images from US inputs.
  • Resultant images showed considerably more discernible details compared to original US images.

Conclusions:

  • Recognizing artifacts as wave interference patterns (WIP) is key to enhancing US image quality.
  • The developed method can simulate complementary imaging modalities in real-time.
  • Further improvements can aid cost-effective, real-time breast cancer diagnosis.