3D Microwave Tomography Using the Soft Prior Regularization Technique: Evaluation in Anatomically-Realistic MRI-Derived Numerical Breast Phantoms
Document Type
Article
Publication Date
1-10-2019
Journal / Book Title
IEEE Transactions on Biomedical Engineering
Abstract
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 3D breast geometries derived from MR breast data with different parenchymal densities, as well as a simulated tumor to evaluate 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 (RRMSE) 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 3D 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.
DOI
10.1109/TBME.2019.2892303
MSU Digital Commons Citation
Golnabi, Amir H.; Meaney, Paul M.; Geimer, Shireen D.; and Paulsen, Keith D., "3D Microwave Tomography Using the Soft Prior Regularization Technique: Evaluation in Anatomically-Realistic MRI-Derived Numerical Breast Phantoms" (2019). Department of Mathematics Facuty Scholarship and Creative Works. 28.
https://digitalcommons.montclair.edu/mathsci-facpubs/28