volume | PIER Journals

Abstract

In this paper, an electrically small magnetic probe combined with principal components analysis (PCA) technique for microwave breast cancer detection is presented. The proposed magnetic probe is designed as an electrically small square loop antenna integrated with a matching network operating at 528 MHz. The concept of the proposed microwave detection is based on the shift in the resonance frequency of the near-field magnetic probe due to the presence of a tumor. The proposed magnetic probe is highly sensitive in detecting any changes or abnormality in the dielectric properties of the female breast tissues. Detecting the existence of the breast tumors is expected by estimating the variations in the scattering parameters of the probe's response. The PCA is a feature extraction technique applied to accentuate the variance in the sensor responses for both healthy and tumorous cases. It is shown that when a numerical realistic breast phantom with and without tumor cells is placed close to the magnetic probe in the near-field region, the probe is capable of distinguishing between healthy and tumorous tissues. In addition, the probe can identify tumors with various sizes placed in a specific location within the breast. As a proof of concept, the magnetic probe was fabricated and used to detect a 9 mm metallic sphere buried at three different locations inside a lump of chicken meat, mimicking both normal and tumorous breast tissues, respectively. The CST numerical simulations and experimental results demonstrate that the presented technique is an emerging modality for detecting breast tumors through an inexpensive and portable way.

1. Society, A. C., "Cancer facts and figures 2019 @ONLINE,", [Online]. Available: https://www.cancer.org/content/dam/cancer-org/research/cancer-facts-andstatistics/annual-cancer-facts-and-figures/2019/cancer-facts-and-figures-2019.pdf, 2019.
doi:10.1109/TEMC.2019.2896519

2. Attaran, A., W. B. Handler, and B. A. Chronik, "2 mm radius loop antenna and linear active balun for near field measurement of magnetic field in MRI-conditional testing of medical devices," IEEE Transactions on Electromagnetic Compatibility, 1-8, 2019.
doi:10.1109/ICEAA.2017.8065655

3. Radder, J., M. Woo, P. Van de Moortele, G. Metzger, A. Ertürk, J. Strupp, K. Ugurbil, and G. Adriany, "Optimization and simulation of a 16-channel loop and dipole array for head MRI applications at 10.5 tesla," 2017 International Conference on Electromagnetics in Advanced Applications (ICEAA), 1828-1831, Sep. 2017.
doi:10.3390/s17071572

4. Wang, L., "Early diagnosis of breast cancer," Sensors, Vol. 17, No. 7, 1572, 2017.
doi:10.1049/iet-map.2017.0599

5. Obermeier, R. and J. A. Martinez-Lorenzo, "Compressive sensing unmixing algorithm for breast cancer detection," IET Microwaves, Antennas & Propagation, Vol. 12, No. 4, 533-541, 2018.
doi:10.1109/TBME.2018.2807799

6. Yousefnia, M., A. Ebrahimzadeh, M. Dehmollaian, and A. Madannejad, "A time-reversal imaging system for breast screening: Theory and initial phantom results," IEEE Transactions on Biomedical Engineering, Vol. 65, No. 11, 2542-2551, 2018.
doi:10.1007/978-3-319-27866-7

7. Conceição, R. C., J. J. Mohr, and M. O'Halloran, An Introduction to Microwave Imaging for Breast Cancer Detection, Springer, 2016.
doi:10.1109/6668.990683

8. Fear, E. C., 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, 48-56, 2002.
doi:10.1109/22.883861

9. Meaney, P. M., M. W. Fanning, D. Li, S. P. Poplack, and K. D. Paulsen, "A clinical prototype for active microwave imaging of the breast," IEEE Transactions on Microwave Theory and Techniques, Vol. 48, No. 11, 1841-1853, 2000.
doi:10.1163/156939304774113089

10. Caorsi, S., M. Donelli, A. Lommi, and A. Massa, "Location and imaging of two-dimensional scatterers by using a particle swarm algorithm," Journal of Electromagnetic Waves and Applications, Vol. 18, No. 4, 481-494, 2004.
doi:10.2528/PIERM11040903

11. Donelli, M., I. J. Craddock, D. Gibbins, and M. Sarafianou, "A three-dimensional time domain microwave imaging method for breast cancer detection based on an evolutionary algorithm," Progress In Electromagnetics Research, Vol. 18, 179-195, 2011.
doi:10.4218/etrij.10.0109.0626

12. Son, S.-H., N. Simonov, H.-J. Kim, J.-M. Lee, and S.-I. Jeon, "Preclinical prototype development of a microwave tomography system for breast cancer detection," ETRI Journal, Vol. 32, No. 6, 901-910, 2010.

13. Bridges, J. E., "Non-invasive system for breast cancer detection,", US Patent 5,704, 355, Jan. 6, 1998.
doi:10.1109/TBME.2002.800759

14. Fear, E. C., 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, 812-822, 2002.
doi:10.2528/PIERM11061206

15. Donelli, M., "A rescue radar system for the detection of victims trapped under rubble based on the independent component analysis algorithm," Progress In Electromagnetics Research, Vol. 19, 173-181, 2011.
doi:10.3390/s18020655

16. Wang, L., "Microwave sensors for breast cancer detection," Sensors, Vol. 18, No. 2, 655, 2018.

17. Klemm, M., I. Craddock, J. Leendertz, A. Preece, and R. Benjamin, "Experimental and clinical results of breast cancer detection using uwb microwave radar," Antennas and Propagation Society International Symposium, 2008. AP-S 2008. IEEE, 1-4, 2008.
doi:10.1109/RBME.2011.2169780

18. Hassan, A. M. and M. El-Shenawee, "Review of electromagnetic techniques for breast cancer detection," IEEE Reviews in Biomedical Engineering, Vol. 4, 103-118, 2011.
doi:10.1038/s41598-018-31046-9

19. Aldhaeebi, M. A., T. S. Almoneef, A. Ali, Z. Ren, and O. M. Ramahi, "Near field breast tumor detection using ultra-narrow band probe with machine learning techniques," Scientific Reports, Vol. 8, No. 1, 12607, 2018.

20. Bourqui, J., J. M. Sill, and E. C. Fear, "A prototype system for measuring microwave frequency reflections from the breast," Journal of Biomedical Imaging, Vol. 2012, 9, 2012.

21. Gazhonova, V., 3D Automated Breast Volume Sonography: A Practical Guide, Springer, 2016.
doi:10.1016/j.mri.2012.10.022

22. Chen, J.-H., S. Chan, D.-C. Yeh, P. T. Fwu, M. Lin, and M.-Y. Su, "Response of bilateral breasts to the endogenous hormonal uctuation in a menstrual cycle evaluated using 3d mri," Magnetic Resonance Imaging, Vol. 31, No. 4, 538-544, 2013.

23. CST "Computer simulation technology. cst computer simulation technology ag@ONLINE,", [Online]. Available: http://www.CST.com, Sep. 2017.

24. UWCEM "Breast phantom repository@ONLINE,", [Online]. Available: http://uwcem.ece.wisc.edu/phantomRepository.html, Aug. 2017.

25. Zastrow, E., S. Davis, M. Lazebnik, F. Kelcz, B. van Veem, and S. Hagness, "Database of 3D grid-based numerical breast phantoms for use in computational electromagnetics simulations,", [Online]. Available: https://uwcem.ece.wisc.edu/MRIdatabase/InstructionManual.pdf, 2008.
doi:10.1109/TBME.2008.2002130

26. Zastrow, E., S. K. Davis, M. Lazebnik, F. Kelcz, B. D. Van Veen, and S. C. Hagness, "Development of anatomically realistic numerical breast phantoms with accurate dielectric properties for modeling microwave interactions with the human breast," IEEE Transactions on Biomedical Engineering, Vol. 55, No. 12, 2792-2800, 2008.
doi:10.1088/0031-9155/52/20/002

27. Lazebnik, M., D. Popovic, L. McCartney, C. B. Watkins, M. J. Lindstrom, J. Harter, S. Sewall, T. Ogilvie, A. Magliocco, T. M. Breslin, et al. "A large-scale study of the ultrawideband microwave dielectric properties of normal, benign and malignant breast tissues obtained from cancer surgeries," Physics in Medicine and Biology, Vol. 52, No. 20, 6093, 2007.

28. Richardson, M., "Principal component analysis,", URL: http://people.maths.ox.ac.uk/richardsonm/SignalProcPCA.pdf (last access: 3.5. 2013), Aleš Hladnik Dr., Ass. Prof., Chair of Information and Graphic Arts Technology, Faculty of Natural Sciences and Engineering, University of Ljubljana, Slovenia, ales.hladnik@ntf. uni-lj.si, 2009.
doi:10.1002/wics.101

29. Abdi, H. and L. J. Williams, "Principal component analysis," Wiley Interdisciplinary Reviews: Computational Statistics, Vol. 2, No. 4, 433-459, 2010.
doi:10.1098/rsta.2015.0202

30. Jolliffe, I. T. and J. Cadima, "Principal component analysis: A review and recent developments," Phil. Trans. R. Soc. A, Vol. 374, No. 2065, 20150202, 2016.

Dr. Maged A. Aldhaeebi

Prof. Thamer S. Almoneef

Dr. Hussein Attia

Professor Omar M. Ramahi