
Novel Multistatic Adaptive Microwave Imaging Methods for Early Breast Cancer Detection
Multistatic adaptive microwave imaging (MAMI) methods are presented and compared for early breast cancer detection. Due to the significant contrast between the dielectric properties of normal and malignant breast tissues, developing microwave imaging techniques for early breast cancer detection has attracted much interest lately.
EURASIP Journal on Applied Signal Processing
Volume 2006 (2006),
doi:10.1155/ASP/2006/91961
Accepted 21 December 2005
Multistatic adaptive microwave imaging (MAMI) methods are presented and compared for early breast cancer detection. Due to the significant contrast between the dielectric properties of normal and malignant breast tissues, developing microwave imaging techniques for early breast cancer detection has attracted much interest lately. MAMI is one of the microwave imaging modalities and employs multiple antennas that take turns to transmit ultra-wideband (UWB) pulses while all antennas are used to receive the reflected signals. MAMI can be considered as a special case of the multi-input multi-output (MIMO) radar with the multiple transmitted waveforms being either UWB pulses or zeros. Since the UWB pulses transmitted by different antennas are displaced in time, the multiple transmitted waveforms are orthogonal to each other. The challenge to microwave imaging is to improve resolution and suppress strong interferences caused by the breast skin, nipple, and so forth. The MAMI methods we investigate herein utilize the data-adaptive robust Capon beamformer (RCB) to achieve high resolution and interference suppression. We will demonstrate the effectiveness of our proposed methods for breast cancer detection via numerical examples with data simulated using the finite-difference time-domain method based on a 3D realistic breast model.
This article was published in the special issue
References:
Linked References
[1] S. J. Nass, I. C. Henderson, and J. C. Lashof, Mammography and Beyond: Developing Techniques for the Early Detection of Breast Cancer, Institute of Medicine, National Academy Press, Washington, DC, USA, 2001.
[2] E. C. Fear, S. C. Hagness, P. M. Meaney, M. Okoniewski, and M. A. Stuchly, “Enhancing breast tumor detection with near-field imaging,” IEEE Microwave Magazine, vol. 3, no. 1, pp. 48â56, 2002. [
[3] C. Gabriel, R. W. Lau, and S. Gabriel, “The dielectric properties of biological tissues: II. Measurements in the frequency range 10 Hz to 20 GHz,” Physics in Medicine and Biology, vol. 41, no. 11, pp. 2251â2269, 1996. [
[4] S. S. Chaudhary, R. K. Mishra, A. Swarup, and J. M. Thomas, “Dielectric properties of normal and malignant human breast tissues at radiowave and microwave frequencies,” Indian Journal of Biochemistry and Biophysics, vol. 21, pp. 76â79, 1984.
[5] W. T. Joines, Y. Zhang, C. Li, and R. L. Jirtel, “The measured electrical properties of normal and malignant human tissues from 50 to 900 MHz,” Medical Physics, vol. 21, no. 4, pp. 547â550, 1994. [
[6] A. J. Surowiec, S. S. Stuchly, J. R. Barr, and A. Swarup, “Dielectric properties of breast carcinoma and the surrounding tissues,” IEEE Transactions on Biomedical Engineering, vol. 35, no. 4, pp. 257â263, 1988. [
[7] A. Swarup, S. S. Stuchly, and A. J. Surowiec, “Dielectric properties of mouse MCA1 fibrosarcoma at different stages of development,” Bioelectromagnetics, vol. 12, no. 1, pp. 1â8, 1991. [
[8] X. Li and S. C. Hagness, “A confocal microwave imaging algorithm for breast cancer detection,” IEEE Microwave and Wireless Components Letters, vol. 11, no. 3, pp. 130â132, 2001. [
[9] B. Guo, Y. Wang, J. Li, P. Stoica, and R. Wu, “Microwave imaging via adaptive beamforming methods for breast cancer detection,” in Proceedings of Progress in Electromagnetics Research Symposium (PIERS '05), Hangzhou, China, August 2005.
[10] B. Guo, Y. Wang, J. Li, P. Stoica, and R. Wu, “Microwave imaging via adaptive beamforming methods for breast cancer detection,” Journal of Electromagnetic Waves and Applications, vol. 20, no. 1, pp. 53â63, 2006. [
[11] R. Nilavalan, A. Gbedemah, I. J. Craddock, X. Li, and S. C. Hagness, “Numerical investigation of breast tumour detection using multi-static radar,” IEE Electronics Letters, vol. 39, no. 25, pp. 1787â1789, 2003. [
[12] E. Fishler, A. Haimovich, R. Blum, D. Chizhik, L. Cimini, and R. Valenzuela, “MIMO radar: an idea whose time has come,” in Proceedings of IEEE Radar Conference, pp. 71â78, Philadelphia, Pa, USA, April 2004.
[13] E. Fishler, A. Haimovich, R. Blum, D. Chizhik, L. Cimini, and R. Valenzuela, “Spatial diversity in radars-models and detection performance,” to appear in IEEE Transactions on Signal Processing.
[14] L. Xu, J. Li, and P. Stoica, “Radar Imaging via Adaptive MIMO Techniques,” in Proceedings of 14th European Signal Processing Conference (EUSIPCO '06), Florence, Italy, September 2006,
[15] E. J. Bond, X. Li, S. C. Hagness, and B. D. Van Veen, “Microwave imaging via space-time beamforming for early detection of breast cancer,” IEEE Transactions on Antennas and Propagation, vol. 51, no. 8, pp. 1690â1705, 2003. [
[16] Y. Xie, B. Guo, L. Xu, J. Li, and P. Stoica, “Multi-static adaptive microwave imaging for early breast cancer detection,” in Proceedings of 39th ASILOMAR Conference on Signals, Systems and Computers, Pacific Grove, Calif, USA, October 2005.
[17] J. Li, P. Stoica, and Z. Wang, “On robust Capon beamforming and diagonal loading,” IEEE Transactions on Signal Processing, vol. 51, no. 7, pp. 1702â1715, 2003. [
[18] P. Stoica, Z. Wang, and J. Li, “Robust Capon beamforming,” IEEE Signal Processing Letters, vol. 10, no. 6, pp. 172â175, 2003. [
[19] J. Li and P. Stoica, Eds., Robust Adaptive Beamforming, John Wiley & Sons, New York, NY, USA, 2005.
[20] E. C. Fear, X. Li, S. C. Hagness, and M. A. Stuchly, “Confocal microwave imaging for breast cancer detection: localization of tumors in three dimensions,” IEEE Transactions on Biomedical Engineering, vol. 49, no. 8, pp. 812â822, 2002. [
[21] E. C. Fear and M. Okoniewski, “Confocal microwave imaging for breast cancer detection: Application to hemispherical breast model,” in Proceedings of IEEE MTT-S International Microwave Symposium Digest, vol. 3, pp. 1759â1762, Seattle, Wash, USA, June 2002.
[22] R. A. Monzingo and T. W. Miller, Introduction to Adaptive Arrays, John Wiley & Sons, New York, NY, USA, 1980.
[23] D. D. Feldman and L. J. Griffiths, “A projection approach for robust adaptive beamforming,” IEEE Transactions on Signal Processing, vol. 42, no. 4, pp. 867â876, 1994. [
[24] P. M. Meaney, “Importance of using a reduced contrast coupling medium in 2D microwave breast imaging,” Journal of Electromagnetic Waves and Applications, vol. 17, no. 2, pp. 333â355, 2003. [
[25] D. M. Sullivan, Electromagnetic Simulation Using FDTD Method, Wiley/IEEE Press, New York, NY, USA, 1st edition, 2000.
[26] A. Taflove and S. C. Hagness, Computational Electrodynamics: The Finite-Difference Time-Domain Method, Artech House, Boston, Mass, USA, 3rd edition, 2005.
[27] S. D. Gedney, “An anisotropic perfectly matched layer-absorbing medium for the truncation of FDTD lattices,” IEEE Transactions on Antennas and Propagation, vol. 44, no. 12, pp. 1630â1639, 1996. [
[28] D. M. Sullivan, “Z-transform theory and the FDTD method,” IEEE Transactions on Antennas and Propagation, vol. 44, no. 1, pp. 28â34, 1996. [
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