The Comparison of Performance between Contrast Source Inversion and Its Related Algorithms and Several Improved Methods

The contrast source inversion (CSI) is an effective method for solving microwave imaging problems which is widely utilized. The core of the CSI is to change the conventional inverse scattering problem into an optimization problem. The two items in the objective function describe the state error and data error, respectively. As it is all known, there is almost no complete performance comparison based on Fresnel data for the CSI and its related improved algorithms. In addition, the performance of the algorithm under different weights was not analyzed before and the convergence speed of original CSI is slow. Firstly, this paper compares the performance of traditional CSI and its improved algorithms from three aspects of qualitative imaging effect, convergence speed, and objective function value based on Fresnel data. Secondly, the influence of the state error and the data error under different weights on the convergence rate and the objective function value are studied. For the limitation of a slower convergence rate, the CSI with weights (W-CSI), the CSI with dynamic reduction factor (CSI-DRF), and its related algorithms, which can get better convergence rate compared with their relative original algorithms, are proposed. Eventually, the future research work is prospected.

A Machine Learning Workflow for Tumour Detection in Breasts Using 3D Microwave Imaging

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.