FDTD analysis of a pulsed microwave confocal system for breast cancer detection

Susan C. Hagness*, Allen Taflove, Jack E. Bridges

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

14 Scopus citations

Abstract

A novel focused active microwave system is investigated for detecting breast cancer. In contrast to x-ray and ultrasound modalities, the method reviewed here exploits the breast-tissue physical properties unique to the microwave spectrum, namely, the translucent nature of normal breast tissues and the high dielectric contrast between malignant and normal breast tissues. The system uses a confocal technique and time-gating to enhance the detection of malignant tumors while suppressing the effects of tissue heterogeneity and absorption. Using published data for the dielectric properties of normal and malignant breast tissues, we have conducted a preliminary 2-D finite-difference time-domain (FDTD) computational electromagnetics analysis of the system. We considered two types of inhomogeneities of normal tissue: a statistically random variation of the dielectric parameters throughout the breast in a ±10% range, and a spatially coherent inhomogeneity representing a vein. The modeled excitation was a pulsed 6-GHz waveform. The FDTD simulations showed that malignant tumors as small as 2 mm in diameter could be robustly detected in the presence of the background clutter generated by the heterogeneity of the surrounding normal tissue. Spatial resolution of the tumor location was found to be in the order of 1 cm.

Original languageEnglish (US)
Pages (from-to)2506-2508
Number of pages3
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume6
StatePublished - 1997
EventProceedings of the 1997 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Chicago, IL, USA
Duration: Oct 30 1997Nov 2 1997

ASJC Scopus subject areas

  • Signal Processing
  • Health Informatics
  • Computer Vision and Pattern Recognition
  • Biomedical Engineering

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