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.
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.
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.
5. Van Den Berg, P. M. and R. E. Kleinman, "A contrast source inversion method," Inverse Problems, Vol. 13, No. 6, 1607, 1997.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
24. Curlander, J. C. and R. N. McDonough, Synthetic Aperture Radar, Vol. 11, Wiley, New York, 1991.
25. Zhdanov, M. S., Geophysical Inverse Theory and Regularization Problems, Vol. 36, Elsevier, 2002.
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.
27. Chen, X., Computational Methods for Electromagnetic Inverse Scattering, Wiley Online Library, 2018.
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.
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.
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.
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.
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.
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.
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.
36. Geddert, N., "An electromagnetic hybridizable discontinuous Galerkin method forward solver with high-order geometry for inverse problems,", 2020.