A Novel Method to Mitigate Real–Imaginary Image Imbalance in Microwave Tomography

Typical microwave tomographic techniques reconstruct the real part of the permittivity with much greater accuracy as compared to the imaginary part. In this paper, we propose a method to mitigate the imbalance between the reconstructed complex permittivity components and increase the accuracy of the overall image recovery. To do so, the complex permittivity in the imaging domain is expressed as a weighted sum of a few preselected permittivities, close to the range of the expected values. To obtain the permittivity weights, a Gauss–Newton algorithm is employed. Image reconstructions from simulated and experimental data for different biomedical phantoms are presented. Results show that the proposed method leads to excellent reconstruction with balanced real and imaginary parts, across different scenarios.

[1]  John L. Volakis,et al.  A modified Gauss‐Newton algorithm for fast microwave imaging using near‐field probes , 2017 .

[2]  Mahta Moghaddam,et al.  Real-time Microwave Imaging of Differential Temperature for Thermal Therapy Monitoring , 2014, IEEE Transactions on Biomedical Engineering.

[3]  Takashi Takenaka,et al.  Inverse scattering for a three-dimensional object in the time domain. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.

[4]  Zhipeng Wu,et al.  Microwave-tomographic system for oil- and gas-multiphase-flow imaging , 2009 .

[5]  Søren Rysgaard,et al.  Imaging air volume fraction in sea ice using non-destructive X-ray tomography , 2015 .

[6]  L. Shafai,et al.  A Near-Field Dual Polarized (TE–TM) Microwave Imaging System , 2013, IEEE Transactions on Microwave Theory and Techniques.

[7]  R. W. Lau,et al.  The dielectric properties of biological tissues: II. Measurements in the frequency range 10 Hz to 20 GHz. , 1996, Physics in medicine and biology.

[8]  Panagiotis Kosmas,et al.  Three-Dimensional Microwave Breast Imaging: Dispersive Dielectric Properties Estimation Using Patient-Specific Basis Functions , 2009, IEEE Transactions on Medical Imaging.

[9]  N. Nikolova.  Microwave Imaging for Breast Cancer , 2011, IEEE Microwave Magazine.

[10]  Manuchehr Soleimani,et al.  Simultaneous reconstruction of permittivity and conductivity using multi-frequency admittance measurement in electrical capacitance tomography , 2016 .

[11]  A. Bulyshev,et al.  Three-dimensional vector microwave tomography: theory and computational experiments , 2004 .

[12]  K. Paulsen,et al.  Nonlinear Microwave Imaging for Breast-Cancer Screening Using Gauss–Newton's Method and the CGLS Inversion Algorithm , 2007, IEEE Transactions on Antennas and Propagation.

[13]  John L. Volakis,et al.  A Novel Method of Deep Tissue Biomedical Imaging Using a Wearable Sensor , 2016, IEEE Sensors Journal.

[14]  Koichi Ito,et al.  Development and characteristics of a biological tissue‐equivalent phantom for microwaves , 2001 .

[15]  Keith D Paulsen,et al.  Pre-scaled two-parameter Gauss-Newton image reconstruction to reduce property recovery imbalance. , 2002, Physics in medicine and biology.

[16]  Amelie Litman,et al.  3-D Imaging of a Microwave Absorber Sample From Microwave Scattered Field Measurements , 2015, IEEE Microwave and Wireless Components Letters.

[17]  Puyan Mojabi,et al.  A Wideband Microwave Tomography System With a Novel Frequency Selection Procedure , 2010, IEEE Transactions on Biomedical Engineering.

[18]  Zaiping Nie,et al.  Subspace-Based Variational Born Iterative Method for Solving Inverse Scattering Problems , 2019, IEEE Geoscience and Remote Sensing Letters.

[19]  John L. Volakis,et al.  A microwave tomographic technique to enhance real-imaginary permittivity image quality , 2017, 2017 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting.

[20]  Ioannis T. Rekanos,et al.  Microwave imaging in the time domain of buried multiple scatterers by using an FDTD-based optimization technique , 2003 .

[21]  Qing Huo Liu,et al.  Three-dimensional nonlinear image reconstruction for microwave biomedical imaging , 2004, IEEE Transactions on Biomedical Engineering.

[22]  Siyuan Chen,et al.  Inverse scattering of two-dimensional dielectric objects buried in a lossy earth using the distorted Born iterative method , 2001, IEEE Trans. Geosci. Remote. Sens..

[23]  Paul M. Meaney,et al.  Microwave Tomographic Imaging Utilizing Low-Profile, Rotating, Right Angle-Bent Monopole Antennas , 2014 .

[24]  Amin M. Abbosh,et al.  A Rapid Medical Microwave Tomography Based on Partial Differential Equations , 2018, IEEE Transactions on Antennas and Propagation.

[25]  R. W. Lau,et al.  The dielectric properties of biological tissues: III. Parametric models for the dielectric spectrum of tissues. , 1996, Physics in medicine and biology.

[26]  Mahta Moghaddam,et al.  Real-Time Three-Dimensional Microwave Monitoring of Interstitial Thermal Therapy , 2018, IEEE Transactions on Biomedical Engineering.

[27]  Manuel Benedetti,et al.  An Innovative Microwave-Imaging Technique for Nondestructive Evaluation: Applications to Civil Structures Monitoring and Biological Bodies Inspection , 2006, IEEE Transactions on Instrumentation and Measurement.

[28]  Paul M. Meaney,et al.  Conformal microwave imaging for breast cancer detection , 2003 .

[29]  Simon Arridge,et al.  Stroke type differentiation using spectrally constrained multifrequency EIT: evaluation of feasibility in a realistic head model , 2014, Physiological measurement.

[30]  Susan C. Hagness,et al.  High-Resolution Microwave Breast Imaging Using a 3-D Inverse Scattering Algorithm With a Variable-Strength Spatial Prior Constraint , 2015, IEEE Transactions on Antennas and Propagation.