Radar-based tumor localization in heterogeneous breast tissue using a 3D permittivity model

Jochen Moll*, Mantalena Sarafianou, Thomas N. Kelly, Viktor Krozer, Ian J. Craddock

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

3 Citations (Scopus)

Abstract

Many imaging algorithms for microwave breast cancer detection are limited by the assumption that the heterogeneity of the female breast is approximated by an effective permittivity during the reconstruction process. Since other modalities like ultrasound or MRI are used in conjunction with microwaves in recent years, it might be possible to estimate a complex 3D permittivity model that can be used within the reconstruction process as prior information. In this paper, we present a novel beamforming procedure for radar-based microwave breast cancer detection that incorporates a 3D-permittivity model. Hence, the reconstruction can take place in time domain on a piecewise basis rather than range domain. This approach is analyzed in this paper by means of a heterogeneous numerical breast phantom using Bristol's 31-element array configuration.

Original languageEnglish
Title of host publication8th European Conference on Antennas and Propagation, EuCAP 2014
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1288-1291
Number of pages4
ISBN (Print)9788890701849
DOIs
Publication statusPublished - 1 Jan 2014
Event8th European Conference on Antennas and Propagation, EuCAP 2014 - The Hague, United Kingdom
Duration: 6 Apr 201411 Apr 2014

Conference

Conference8th European Conference on Antennas and Propagation, EuCAP 2014
CountryUnited Kingdom
CityThe Hague
Period6/04/1411/04/14

Structured keywords

  • Digital Health

Keywords

  • Breast Cancer Detection
  • FDTD
  • Microwave Imaging
  • Multistatic Radar System
  • Signal Focusing

Fingerprint Dive into the research topics of 'Radar-based tumor localization in heterogeneous breast tissue using a 3D permittivity model'. Together they form a unique fingerprint.

Cite this