U-net for spectral quantitative microwave breast imaging

TL;DR

This paper introduces a spectral Fourier diffraction-based approach combined with U-NET neural networks for quantitative microwave breast imaging, demonstrating significant improvements in image quality on realistic breast phantom data.

Contribution

It presents a novel integration of spectral Fourier diffraction methods with U-NETs trained on realistic breast data for enhanced microwave imaging.

Findings

Major improvement in imaging accuracy with U-NET integration

Effective training on spectral database of breast components

Preliminary numerical results validate the approach

Abstract

A spectral approach based on the Fourier diffraction theorem is combined with a pair of U-NETs to perform quantitative microwave imaging of an anthropomorphic breast phantom. The U-NET pair is trained on a spectral database constructed from combinations of different realistic parts of the breast. Some preliminary numerical results are presented to show the major improvement brought by the U-NET.