3-D Microwave Tomography Using the Soft Prior Regularization Technique: Evaluation in Anatomically Realistic MRI-Derived Numerical Breast Phantoms
Artikel i vetenskaplig tidskrift, 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.
soft prior regularization