Abstract
A two-stage workflow for detecting and monitoring tumors in the human breast with an inverse scattering-based technique is presented. Stage 1 involves a phaseless bulk-parameter inference neural network that recovers the geometry and permittivity of the breast fibroglandular region. The bulk parameters are used for calibration and as prior information for Stage 2, a full phase contrast source inversion of the measurement data, to detect regions of high relative complex-valued permittivity in the breast based on an assumed known overall tissue geometry. We demonstrate the ability of the workflow to recover the geometry and bulk permittivity of the different sized fibroglandular regions, and to detect and localize tumors of various sizes and locations within the breast model. Preliminary results show promise for a synthetically trained Stage 1 network to be applied to experimental data and provide quality prior information in practical imaging situations.
Highlights
There are generally two forms of microwave imaging (MWI) for the detection of breast cancer [1]: radar-based [2,3] and inverse scattering-based techniques [4,5,6,7,8,9]
The overarching goal of this work is to automate the extraction of the bulk parameters for the breast imaging problem, the primary focus is to generate a neural network model that can automate the successful recovery of prior information about the fibroglandular region of a target which may or may not contain a tumor
We have presented a framework for microwave imaging that is capable of detecting the presence of, and monitoring the size of tumors in a model of the human breast
Summary
There are generally two forms of microwave imaging (MWI) for the detection of breast cancer [1]: radar-based [2,3] and inverse scattering-based techniques [4,5,6,7,8,9]. The latter, which is the subject of this work, aims to reconstruct an image of the different tissue regions within the breast in the form of a quantitative map of the complex permittivities within the region of interest (ROI). Parallel finite-element method (FEM) or discontinuous Galerkin method (DGM) based algorithms have been applied to realistic biomedical imaging scenarios for breast cancer monitoring [12,13,14]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
