A Multi-Threshold Iterative DBIM-Based Algorithm for the Imaging of Heterogeneous Breast Tissues

Microwave imaging (MWI) represents a well-known tool for quantitatively retrieving unknown objects in a nondestructive way. Microwave radiation is non-ionizing, which suggests that MWI can be also attractive for medical diagnostics applications. This work proposes a novel MWI multi-frequency technique, which combines compressive sensing (CS) with the well-known distorted Born iterative method (DBIM). CS strategies are emerging as a promising tool in MWI applications, which can improve reconstruction quality and/or reduce the number of data samples. The proposed approach is based on iterative shrinkage thresholding algorithm (ISTA), which has been modified to include an automatic and adaptive selection of multithreshold values. This adaptive multi-threshold ISTA (AMTISTA) implementation is applied in reconstruction of two-dimensional (2-D) numerical heterogeneous breast phantoms, where it outerperforms the standard thresholding implementation. We show that our approach is also successful in three-dimensional (3-D) simulations of a realistic imaging experiment, despite the mismatch between the data and our algorithm's forward model. These results suggest that the proposed algorithm is a promising tool for medical MWI applications.