volume | PIER Journals

Abstract

We demonstrate the recovery of simple geometric and permittivity information of breast models in an experimental microwave breast imaging system using a synthetically trained machine learning workflow. The recovered information consists of simple models of adipose and fibroglandular regions. The machine learning model is trained on a labelled synthetic dataset constructed over a range of possible adipose and fibroglandular regions and the trained neural network predicts the geometry and average permittivty of the adipose and fibroglandular regions from calibrated experimental data. The proposed workflow is tested on two different experimental models of the human breast. The first model is comprised of two simple, symmetric phantoms representing the adipose and fibroglandular regions of the breast that match the model used to train the neural network. The second, more realistic model replaces the symmetric fibroglandular phantom with an irregularly shaped, MRI-derived fibroglandular phantom. We demonstrate the ability of the machine learning workflow to accurately recover geometry and complex valued average permittivity of the fibroglandular region for the simple case, and to predict a symmetric convex hull that is a reasonable approximation to the proportions of the MRI-derived fibroglandular phantom.

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Miss Keeley Edwards

Dr. Joe LoVetri

Dr. Colin Gilmore

Dr. Ian Jeffrey