ArticleIEEE transactions on bio-medical engineering2019
3-D Microwave Tomography Using the Soft Prior Regularization Technique: Evaluation in Anatomically Realistic MRI-Derived Numerical Breast Phantoms.
Article in IEEE transactions on bio-medical engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Generation of Prior Information in a Dual-Mode Microwave-Ultrasound Breast Imaging System.Sensors (Basel, Switzerland) · 2022Article
- Using prior information to enhance microwave tomography images in bone health assessment.Biomedical engineering online · 2022Article
- Full 3D Microwave Breast Imaging Using a Deep-Learning Technique.Journal of imaging · 2020Article
- On the Orbital Angular Momentum Incident Fields in Linearized Microwave Imaging.Sensors (Basel, Switzerland) · 2020Article
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Authors and funding
4 authors.
Funding
Abstract
objectiveFusion 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.
methodsNumerical 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.
resultsWhen 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.
conclusionThis 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.
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