3-D Microwave Tomography Using the Soft Prior Regularization Technique: Evaluation in Anatomically Realistic MRI-Derived Numerical Breast Phantoms
Journal article, 2019

Objective: Fusion of magnetic resonance imaging (MRI) breast images with microwave tomography is accomplished through a soft prior technique, which incorporates spatial information (from MRI), i. e., accurate boundary location of different regions of interest, into the regularization process of the microwave image reconstruction algorithm.

Methods: Numerical experiments were completed on a set of three-dimensional (3-D) breast geometries derived from MR breast data with different parenchymal densities, as well as a simulated tumor to evaluate the performance over a range of breast shapes, sizes, and property distributions.

Results: When the soft prior regularization technique was applied, both permittivity and conductivity relative root mean square error values decreased by more than 87% across all breast densities, except in two cases where the error decrease was only 55% and 78%. In addition, the incorporation of structural priors increased contrast between tumor and fibroglandular tissue by 59% in permittivity and 192% in conductivity. Conclusion: This study confirmed that the soft prior algorithm is robust in 3-D and can function successfully across a range of complex geometries and tissue property distributions.

Significance: This study demonstrates that our microwave tomography is capable of recovering accurate tissue property distributions when spatial information from MRI is incorporated through soft prior regularization.

multimodality imaging


soft prior regularization

Breast cancer

microwave tomography


Amir H. Golnabi

Montclair State Univ, Dept Math Sci

Paul M Meaney

Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik, Biomedical Electromagnetics

Shireen D. Geimer

Dartmouth College

Keith D. Paulsen

Dartmouth College

IEEE Transactions on Biomedical Engineering

0018-9294 (ISSN)

Vol. 66 9 2566-2575

Subject Categories

Radiology, Nuclear Medicine and Medical Imaging

Computer Vision and Robotics (Autonomous Systems)

Medical Image Processing





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