Nonlinear microwave imaging heavily relies on an accurate numerical electromagnetic model of the antenna system. The model is used to simulate scattering data that is compared to its measured counterpart in order to reconstruct the image. In this paper an antenna system immersed in water is used to image different canonical objects in order to investigate the implication of modeling errors on the final reconstruction using a time domain-based iterative inverse reconstruction algorithm and three-dimensional FDTD modeling. With the test objects immersed in a background of air and tap water, respectively, we have studied the impact of antenna modeling errors, errors in the modeling of the background media, and made a comparison with a two-dimensional version of the algorithm. In conclusion even small modeling errors in the antennas can significantly alter the reconstructed image. Since the image reconstruction procedure is highly nonlinear general conclusions are very difficult to make. In our case it means that with the antenna system immersed in water and using our present FDTD-based electromagnetic model the imaging results are improved if refraining from modeling the water-wall-air interface and instead just use a homogeneous background of water in the model. 1. Introduction Microwave imaging has received significant attention in the research community during the last couple of decades as a modality that potentially could improve the diagnostics of, for example, breast cancer tumors. Recent progress in the field has been reviewed in [1] and [2]. Today the research has come to the stage where early clinical trials have been and are being performed, [3–6]. The results from the clinical work are promising, but further development of the measurement systems as well as of the image reconstruction algorithms remains before the technique can be considered for daily clinical practice. When performing microwave tomography the aim is to quantitatively reconstruct the dielectric parameters in the region under test. This involves solving a computationally challenging nonlinear and ill-posed optimization problem. The image reconstruction algorithm utilizes measured data that are compared against a corresponding numerical simulation of the system, and the dielectric profile is iteratively updated based on the difference between the simulation and the measurement. Even though this comparison requires a realistic numerical model for the best accuracy, most of the published works have used 2D models together with a calibration procedure to enable the comparison with
References
[1]
E. C. Fear, P. M. Meaney, and M. A. Stuchly, “Microwaves for breast cancer detection?” IEEE Potentials, vol. 22, no. 1, pp. 12–18, 2003.
[2]
N. K. Nikolova, “Microwave imaging for breast cancer,” IEEE Microwave Magazine, vol. 12, no. 7, pp. 78–94, 2011.
[3]
M. Klemm, I. Craddock, J. Leendertz, A. Preece, and R. Benjamin, “Experimental and clinical results of breast cancer detection using UWB microwave radar,” in Proceedings of the IEEE Antennas and Propagation Society International Symposium, vol. 1–9, pp. 3014–3017, July 2008.
[4]
P. M. Meaney, M. W. Fanning, T. Raynolds et al., “Initial clinical experience with microwave breast imaging in women with normal mammography,” Academic Radiology, vol. 14, no. 2, pp. 207–218, 2007.
[5]
S. P. Poplack, T. D. Tosteson, W. A. Wells et al., “Electromagnetic breast imaging: results of a pilot study in women with abnormal mammograms,” Radiology, vol. 243, no. 2, pp. 350–359, 2007.
[6]
M. Shere, A. Preece, I. Craddock, J. Leendertz, and M. Klemm, “Multistatic radar: first trials of a new breast imaging modality,” Breast Cancer Research, vol. 11, supplement 2, article 05, 2009.
[7]
Q. Fang, P. M. Meaney, and K. D. Paulsen, “Viable three-dimensional medical microwave tomography: theory and numerical experiments,” IEEE Transactions on Antennas and Propagation, vol. 58, no. 2, pp. 449–458, 2010.
[8]
T. Rub？k, O. S. Kim, and P. Meincke, “Computational validation of a 3-D microwave imaging system for breast-cancer screening,” IEEE Transactions on Antennas and Propagation, vol. 57, no. 7, pp. 2105–2115, 2009.
[9]
S. Y. Semenov, A. E. Bulyshev, A. Abubakar et al., “Microwave-tomographic imaging of the high dielectric-contrast objects using different image-reconstruction approaches,” IEEE Transactions on Microwave Theory and Techniques, vol. 53, no. 7, pp. 2284–2293, 2005.
[10]
D. W. Winters, J. D. Shea, P. Kosmas, B. D. van Veen, and S. C. Hagness, “Three-dimensional microwave breast imaging: dispersive dielectric properties estimation using patient-specific basis functions,” IEEE Transactions on Medical Imaging, vol. 28, no. 7, pp. 969–981, 2009.
[11]
Z. Q. Zhang and Q. H. Liu, “Three-dimensional nonlinear image reconstruction for microwave biomedical imaging,” IEEE Transactions on Biomedical Engineering, vol. 51, no. 3, pp. 544–548, 2004.
[12]
A. Fhager, S. K. Padhi, and J. Howard, “3D image reconstruction in microwave tomography using an efficient FDTD model,” IEEE Antennas and Wireless Propagation Letters, vol. 8, pp. 1353–1356, 2009.
[13]
A. Taove and S. C. Hagness, Computational Electrodynamics: The Finite-Difference Time-Domain Method, Artech House, Boston, Mass, USA, 3rd edition, 2005.
[14]
Q. Fang, P. M. Meaney, S. D. Geimer, S. Y. Streltsov, and K. D. Paulsen, “Microwave image reconstruction from 3D fields coupled to 2D parameter estimation,” IEEE Transactions on Medical Imaging, vol. 23, pp. 475–484, 2004.
[15]
C. T. Liauh, R. G. Hills, and R. B. Roemer, “Comparison of the adjoint and influence coefficient methods for solving the inverse hyperthermia problem,” Journal of Biomechanical Engineering, vol. 115, no. 1, pp. 63–71, 1993.
[16]
A. Fhager and M. Persson, “Comparison of two image reconstruction algorithms for microwave tomography,” Radio Science, vol. 40, no. 3, Article ID RS3017, 2005.
[17]
A. Fhager, P. Hashemzadeh, and M. Persson, “Reconstruction quality and spectral content of an electromagnetic time-domain inversion algorithm,” IEEE Transactions on Biomedical Engineering, vol. 53, no. 8, pp. 1594–1604, 2006.
[18]
R. M. M？kinen, J. S. Juntunen, and M. A. Kivikoski, “An improved thin-wire model for FDTD,” IEEE Transactions on Microwave Theory and Techniques, vol. 50, no. 5, pp. 1245–1255, 2002.
[19]
J. Juntunen, “Note on the S-parameter and input impedance extraction in antenna simulations using FDTD,” Microwave and Optical Technology Letters, vol. 28, pp. 8–11, 2001.
[20]
O. P. M. Pekonen and J. Xu, “Rigorous analysis of circuit parameter extraction from an FDTD simulation excited with a resistive voltage source,” Microwave and Optical Technology Letters, vol. 12, no. 4, pp. 205–210, 1996.
[21]
S. Nordebo, A. Fhager, M. Gustafsson, and M. Persson, “Measured antenna response of a proposed microwave tomography system using an efficient 3-D FDTD model,” IEEE Antennas and Wireless Propagation Letters, vol. 7, pp. 689–692, 2008.
