We demonstrate the recovery of simple geometric and permittivity information of breast models in an experimental microwave breast imaging system using a synthetically trained machine learning workflow. The recovered information consists of simple models of adipose and fibroglandular regions. The machine learning model is trained on a labelled synthetic dataset constructed over a range of possible adipose and fibroglandular regions and the trained neural network predicts the geometry and average permittivty of the adipose and fibroglandular regions from calibrated experimental data. The proposed workflow is tested on two different experimental models of the human breast. The first model is comprised of two simple, symmetric phantoms representing the adipose and fibroglandular regions of the breast that match the model used to train the neural network. The second, more realistic model replaces the symmetric fibroglandular phantom with an irregularly shaped, MRI-derived fibroglandular phantom. We demonstrate the ability of the machine learning workflow to accurately recover geometry and complex valued average permittivity of the fibroglandular region for the simple case, and to predict a symmetric convex hull that is a reasonable approximation to the proportions of the MRI-derived fibroglandular phantom.
1. Shea, J. D., P. Kosmas, S. C. Hagness, and B. D. Van Veen, "Three-dimensional microwave imaging of realistic numerical breast phantoms via a multiple-frequency inverse scattering technique," Medical Physics (Lancaster), Vol. 37, No. 8, 4210-4226, 2010.
2. Asefi, M., A. Baran, and J. LoVetri, "An experimental phantom study for air-based quasi-resonant microwave breast imaging," IEEE Transactions on Microwave Theory and Techniques, Vol. 67, No. 9, 3946-3954, 2019. doi:10.1109/TMTT.2019.2906619
3. AlSawaftah, N., S. El-Abed, S. Dhou, and A. Zakaria, "Microwave imaging for early breast cancer detection: Current state, challenges, and future directions," Journal of Imaging, Vol. 8, No. 5, 2022, [Online], Available: https://www.mdpi.com/2313-433X/8/5/123. doi:10.3390/jimaging8050123
4. Lazebnik, M., 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 & Biology, Vol. 52, No. 20, 6093, 2007. doi:10.1088/0031-9155/52/20/002
5. Van Den Berg, P. M. and R. E. Kleinman, "A contrast source inversion method," Inverse Problems, Vol. 13, No. 6, 1607, 1997. doi:10.1088/0266-5611/13/6/013
6. Zakaria, A., C. Gilmore, and J. LoVetri, "Finite-element contrast source inversion method for microwave imaging," Inverse Problems, Vol. 26, No. 11, 115010, 2010. doi:10.1088/0266-5611/26/11/115010
7. Rubaek, T., P. M. Meaney, P. Meincke, and K. D. Paulsen, "Nonlinear microwave imaging for breast-cancer screening using Gauss-Newton's method and the CGLS inversion algorithm," IEEE Transactions on Antennas and Propagation, Vol. 55, No. 8, 2320-2331, 2007. doi:10.1109/TAP.2007.901993
8. Abubakar, A., T. M. Habashy, G. Pan, M.-K. Li, and , "Application of the multiplicative regularized Gauss-Newton algorithm for three-dimensional microwave imaging," IEEE Transactions on Antennas and Propagation, Vol. 60, No. 5, 2431-2441, 2012. doi:10.1109/TAP.2012.2189712
9. Meaney, P. M. and K. D. Paulsen, "Theoretical premises and contemporary optimizations of microwave tomography," Microwave Technologies, Ch. 14, D. A. Kishk and D. K. H. Yeap, Eds., IntechOpen, Rijeka, 2022, [Online], Available: https://doi.org/10.5772/intechopen.103011.
10. Abdollahi, N., D. Kurrant, P. Mojabi, M. Omer, E. Fear, and J. LoVetri, "Incorporation of ultrasonic prior information for improving quantitative microwave imaging of breast," IEEE Journal on Multiscale and Multiphysics Computational Techniques, Vol. 4, 98-110, 2019. doi:10.1109/JMMCT.2019.2905344
11. Kurrant, D., A. Baran, J. LoVetri, and E. Fear, "Integrating prior information into microwave tomography Part 1: Impact of detail on image quality," Medical Physics, Vol. 44, No. 12, 6461-6481, 2017, [Online], Available: https://aapm.onlinelibrary.wiley.com/doi/abs/10.1002/mp.12585. doi:10.1002/mp.12585
12. Kurrant, D., E. Fear, A. Baran, and J. LoVetri, "Integrating prior information into microwave tomography Part 2: Impact of errors in prior information on microwave tomography image quality," Medical Physics (Lancaster), Vol. 44, No. 12, 6482-6503, 2017.
13. Ostadrahimi, M., P. Mojabi, A. Zakaria, J. LoVetri, and L. Shafai, "Enhancement of Gauss-Newton inversion method for biological tissue imaging," IEEE Transactions on Microwave Theory and Techniques, Vol. 61, No. 9, 3424-3434, 2013. doi:10.1109/TMTT.2013.2273758
14. Neira, L. M., B. D. Van Veen, and S. C. Hagness, "High-resolution microwave breast imaging using a 3-D inverse scattering algorithm with a variable-strength spatial prior constraint," IEEE Transactions on Antennas and Propagation, Vol. 65, No. 11, 6002-6014, 2017. doi:10.1109/TAP.2017.2751668
15. Edwards, K., N. Geddert, K. Krakalovich, R. Kruk, M. Asefi, J. Lovetri, C. Gilmore, I. Jeffrey, and , "Stored grain inventory management using neural-network-based parametric electromagnetic inversion," IEEE Access, Vol. 8, 207182-207192, 2020. doi:10.1109/ACCESS.2020.3038312
16. Li, L., L. Wang, F. Teixeira, L. Che, and T. Cui, "DeepNIS: Deep neural network for nonlinear electromagnetic inverse scattering," IEEE Transactions on Antennas and Propagation, Vol. 67, No. 3, 1819-1825, 2018. doi:10.1109/TAP.2018.2885437
17. Wei, Z. and X. Chen, "Deep-learning schemes for full-wave nonlinear inverse scattering problems," IEEE Transactions on Geoscience and Remote Sensing, Vol. 57, No. 4, 1849-1860, 2019. doi:10.1109/TGRS.2018.2869221
18. Khoshdel, V., M. Asefi, A. Ashraf, and J. LoVetri, "Full 3D microwave breast imaging using a deep-learning technique," Journal of Imaging, Vol. 6, No. 8, 80, Aug. 2020, [Online], Available: http://dx.doi.org/10.3390/jimaging6080080. doi:10.3390/jimaging6080080
19. Khoshdel, V., M. Asefi, A. Ashraf, and J. LoVetri, "A multi-branch deep convolutional fusion architecture for 3D microwave inverse scattering: Stored grain application," Neural Computing and Applications, 2021, [Online], Available: https://doi.org/10.1007/s00521-021-05970-3.
20. Guo, R., Z. Lin, T. Shan, X. Song, M. Li, F. Yang, S. Xu, and A. Abubakar, "Physics embedded deep neural network for solving full-wave inverse scattering problems," IEEE Transactions on Antennas and Propagation, Early Access Article, 1-1, 2021.
21. Zhou, Y., Y. Zhong, Z.Wei, T. Yin, and X. Chen, "An improved deep learning scheme for solving 2- D and 3-D inverse scattering problems," IEEE Transactions on Antennas and Propagation, Vol. 69, No. 5, 2853-2863, 2021. doi:10.1109/TAP.2020.3027898
22. Benny, R., T. A. Anjit, and P. Mythili, "An overview of microwave imaging for breast tumor detection," Progress In Electromagnetics Research, Vol. 87, 61-91, 2020. doi:10.2528/PIERB20012402
23. Gilmore, C., M. Asefi, J. Paliwal, and J. LoVetri, "Industrial scale electromagnetic grain bin monitoring," Computers and Electronics in Agriculture, Vol. 136, 210-220, 2017. doi:10.1016/j.compag.2017.03.005
24. Curlander, J. C. and R. N. McDonough, Synthetic Aperture Radar, Vol. 11, Wiley, New York, 1991.
26. Guo, R., X. Song, M. Li, F. Yang, S. Xu, and A. Abubakar, "Supervised descent learning technique for 2-D microwave imaging," IEEE Transactions on Antennas and Propagation, Vol. 67, No. 5, 3550-3554, 2019. doi:10.1109/TAP.2019.2902667
28. Nemez, K., M. Asefi, A. Baran, and J. LoVetri, "A faceted magnetic field probe resonant chamber for 3D breast MWI: A synthetic study," 2016 17th International Symposium on Antenna Technology and Applied Electromagnetics (ANTEM), 1-3, IEEE, 2016.
29. Chen, X., Z. Wei, M. Li, and P. Rocca, "A review of deep learning approaches for inverse scattering problems (invited review)," Progress In Electromagnetics Research, Vol. 167, 67-81, 2020. doi:10.2528/PIER20030705
30. LoVetri, J., M. A. Asefi, C. Gilmore, and I. Jeffrey, "Innovations in electromagnetic imaging technology: The stored-grain-monitoring case," IEEE Antennas and Propagation Magazine, Vol. 62, No. 5, 33-42, 2020. doi:10.1109/MAP.2020.3003206
31. Li, M., R. Guo, K. Zhang, Z. Lin, F. Yang, S. Xu, X. Chen, A. Massa, and A. Abubakar, "Machine learning in electromagnetics with applications to biomedical imaging: A review," IEEE Antennas and Propagation Magazine, Vol. 63, No. 3, 39-51, 2021. doi:10.1109/MAP.2020.3043469
32. Khoshdel, V., A. Ashraf, and J. LoVetri, "Enhancement of multimodal microwave-ultrasound breast imaging using a deep-learning technique," Sensors, Vol. 19, No. 18, 4050, 2019. doi:10.3390/s19184050
33. Edwards, K., V. Khoshdel, M. Asfi, J. LoVetri, C. Gilmore, and I. Jeffrey, "A machine learning workflow for tumour detection in breasts using 3D microwave imaging," Electronics, Vol. 10, No. 6, 2021, [Online], Available: https://www.mdpi.com/2079-9292/10/6/674. doi:10.3390/electronics10060674
34. Reimer, T., M. Solis, and S. Pistorius, "The application of an iterative structure to the delay-and-sum and the delay-multiply-and-sum beamformers in breast microwave imaging," Diagnostics, Vol. 10, 411, June 2020. doi:10.3390/diagnostics10060411
35. Zakaria, A., I. Jeffrey, J. LoVetri, and A. Zakaria, "Full-vectorial parallel finite-element contrast source inversion method," Progress In Electromagnetics Research, Vol. 142, 463-483, 2013. doi:10.2528/PIER13080706
36. Geddert, N., "An electromagnetic hybridizable discontinuous Galerkin method forward solver with high-order geometry for inverse problems,", 2020.