A Combined Algorithm for High Resolution Microwave Breast Imaging Using Eigenfunction-based Prior

A new algorithm for quantitative microwave breast imaging and cancer detection is presented that is based on creating prior information for the finite element contrast source inversion using a non-iterative eigenfunction-based technique. The prior information is introduced as a numerical inhomogeneous background that modifies the contrast and contrast sources variables being inverted by the finite element contrast source inversion. It is found that this type of specialized physics-based prior, that uses the eigenfunctions of the Helmholtz operator, ensures the stability of the subsequent contrast source inversion and produces high-accuracy and high-resolution breast images. The performance of the combined algorithm is verified by simulation studies of a 2D MRI-derived anthropomorphic breast model.

[1]  B. Pogue,et al.  Microwave image reconstruction utilizing log-magnitude and unwrapped phase to improve high-contrast object recovery , 2001, IEEE Transactions on Medical Imaging.

[2]  C. Curtis,et al.  Microwave Breast Imaging With a Monostatic Radar-Based System: A Study of Application to Patients , 2013, IEEE Transactions on Microwave Theory and Techniques.

[3]  E. Fear,et al.  Regional estimation of the dielectric properties of inhomogeneous objects using near-field reflection data , 2012 .

[4]  Susan C. Hagness,et al.  High-Resolution Microwave Breast Imaging Using a 3-D Inverse Scattering Algorithm With a Variable-Strength Spatial Prior Constraint , 2017 .

[5]  M. Lindstrom,et al.  A large-scale study of the ultrawideband microwave dielectric properties of normal, benign and malignant breast tissues obtained from cancer surgeries , 2007, Physics in medicine and biology.

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

[7]  Joe LoVetri,et al.  Regularization Approaches for a Non-Iterative Eigenfunction-Based Electromagnetic Inversion Algorithm , 2018, 2018 18th International Symposium on Antenna Technology and Applied Electromagnetics (ANTEM).

[8]  C. Pichot,et al.  Inverse scattering: an iterative numerical method for electromagnetic imaging , 1991 .

[9]  C. Gilmore,et al.  Finite-element contrast source inversion method for microwave imaging , 2010 .

[10]  Amir H. Golnabi,et al.  3D microwave tomography of the breast using prior anatomical information. , 2016, Medical physics.

[12]  E. Fear,et al.  Incorporation of Ultrasonic Prior Information for Improving Quantitative Microwave Imaging of Breast , 2019, IEEE Journal on Multiscale and Multiphysics Computational Techniques.

[13]  Joe LoVetri,et al.  Integrating prior information into microwave tomography part 2: Impact of errors in prior information on microwave tomography image quality , 2017, Medical physics.

[14]  X. Li,et al.  Confocal microwave imaging for breast cancer detection: localization of tumors in three dimensions , 2002, IEEE Transactions on Biomedical Engineering.

[15]  Joe LoVetri,et al.  Integrating prior information into microwave tomography Part 1: Impact of detail on image quality , 2017, Medical physics.

[16]  Amir H. Golnabi,et al.  Tomographic Microwave Imaging With Incorporated Prior Spatial Information , 2013, IEEE Transactions on Microwave Theory and Techniques.