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

Abstract

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.

An Overview of Microwave Imaging for Breast Tumor Detection

“…have been reported [87]. The DBIM-ISTA scheme was modified by Ambrosiano et al to include an automatic and adaptive selection of multithreshold values which outperformed the standard thresholding implementation [88,89]. A DBIM-TWIST combination was applied by researchers from King's College, London to develop and validate a 3D experimental setup [90,91].…”

Review of Microwaves Techniques for Breast Cancer Detection

“…Some other studies as shown in Table A2 focussed on the algorithms and methodologies employed for breast cancer detection, rather than on the hardware apparatus. This table summarizes the most recent innovative and effective methodologies for breast cancer imaging [85][86][87][88][89][90][91][92][93][94][95][96][97][98][99][100][101]. Those methodologies aimed at achieving multi-resolution images.…”

“…Those methodologies aimed at achieving multi-resolution images. Furthermore, some of these methodologies adopted magnetic nanoparticles [87,88] and compressive sensing tools [97] to enhance the detection process, by exploiting linear approximations of the inverse scattering problem or iteratively solving a set of linear problems [99][100][101].…”

Multiresolution Virtual Experiments for Microwave Imaging of Complex Scenarios

“…Once Equation 2is solved by adopting a convenient regularization technique, the contrast update is pursued by adding the reconstructed perturbations to the current reference scenario, namely χ k+1 = χ k + ∆χ k . Note that, depending on the a priori information about the target, different regularization techniques can be adopted to solve Equation (2). Finally, the procedure continues until the stopping criterion is fulfilled.…”

“…Notably, these difficulties are worsened when the unknown profile is highly heterogeneous. Many strategies have been proposed in the literature to tackle both non-linearity and ill-posedness of inverse scattering problems, especially in the case of complex scenarios, such as the cases dealing with biomedical imaging or underground inspection [2][3][4][5][6][7][8][9][10].…”