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Full 3D Microwave Breast Imaging Using a Deep-Learning Technique.

Vahab Khoshdel1, Mohammad Asefi1, Ahmed Ashraf1

  • 1Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada.

Journal of Imaging
|August 30, 2021
PubMed
Summary

A deep learning U-Net model enhances 3D microwave breast imaging by reducing artifacts and improving tumor visibility. This artificial intelligence approach shows promise for more accurate breast cancer detection using complex-valued permittivity images.

Keywords:
convolutional neural networksdeep learningimage reconstructionmicrowave breast imagingtumor detection

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Microwave imaging offers a non-ionizing method for breast cancer detection.
  • Contrast-Source Inversion (CSI) is a reconstruction technique used in microwave imaging.
  • Artifacts in CSI reconstructions can hinder accurate tumor detection.

Purpose of the Study:

  • To develop and evaluate a deep learning technique for enhancing 3D breast permittivity images obtained via microwave imaging.
  • To improve the accuracy of tumor detection by reducing artifacts in Contrast-Source Inversion (CSI) reconstructions.
  • To extend a previously developed 2D image enhancement technique to 3D.

Main Methods:

  • A 3D Convolutional Neural Network (CNN) based on the U-Net architecture was employed.
  • The U-Net model was trained using synthetic 3D CSI images and corresponding ground truth numerical phantoms.
  • The network was tested on both synthetic and experimental 3D microwave imaging data.

Main Results:

  • The 3D U-Net effectively reduced artifacts typical of CSI reconstructions.
  • Tumor detectability was significantly enhanced in the processed images.
  • The model demonstrated good performance on both synthetic and experimental data, despite being trained solely on synthetic data.

Conclusions:

  • Deep learning, specifically a 3D U-Net, is a powerful tool for enhancing 3D microwave breast imaging.
  • The developed technique improves image quality and aids in the detection of breast tumors.
  • The approach shows potential for clinical application in breast cancer diagnostics.