A compressive sensing approach for enhancing breast cancer detection using a hybrid DBT / NRI configuration

TL;DR

This paper introduces a hybrid DBT/NRI breast cancer imaging method that employs compressive sensing to reduce sensor count and bandwidth while maintaining detection accuracy.

Contribution

It presents a novel combination of compressive sensing with a hybrid DBT/NRI system for improved breast cancer detection.

Findings

Reduces number of sensing antennas needed

Maintains detection performance with less bandwidth

Uses dielectric properties from DBT in NRI modeling

Abstract

This work presents a novel breast cancer imaging approach that uses compressive sensing in a hybrid Digital Breast Tomosynthesis (DBT) / Nearfield Radar Imaging (NRI) system configuration. The non-homogeneous tissue distribution of the breast, described in terms of dielectric constant and conductivity, is extracted from the DBT image, and it is used by a full-wave Finite Difference in the Frequency Domain (FDFD) method to build a linearized model of the non-linear NRI imaging problem. The inversion of the linear problem is solved using compressive sensing imaging techniques, which lead to a reduction on the required number of sensing antennas and operational bandwidth without loss of performance.