Title
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 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.
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 Mathematical Sciences Faculty Scholarship and Creative Works. 28.
https://digitalcommons.montclair.edu/mathsci-facpubs/28
DOI
10.1109/TBME.2019.2892303