Numerical Breast Research Articles

Synthesising two-dimensional mammographic images using compressed sensing-reconstructed digital breast tomosynthesis images

This study presents a method synthesizing a two-dimensional (2D) mammographic image of reasonable quality directly from a 3D digital breast tomosynthesis (DBT) image, reconstructed using an advanced compressed sensing (CS)-based algorithm. This approach aims to reduce the radiation dose required for complementary imaging technologies of digital mammography (DM) and DBT, eliminating the need for additional DM examinations. The method involves three main steps: projection data acquisition from a DBT system, CS-based DBT reconstruction, and synthesis of a 2D mammographic image from the reconstructed DBT image. To verify the efficacy of the proposed method, we conducted both a simulation and an experiment on a numerical breast and commercially available BR3D phantoms, respectively, prior to practical implementation in real-world DBT systems. Our simulation and experimental results indicated that the CS-based algorithm yielded markedly improved DBT reconstruction quality, preserving superior image homogeneity, better edge contour and sharpening, and fewer image artifacts. The measured contrast-to-noise ratio and structural similarity values of the CS-reconstructed DBT images were 10.31 and 0.78, respectively, which were approximately 2.2 and 3.1 times larger, respectively, than those of the filtered-backprojection-reconstructed DBT images in the simulation. The quality of the synthetic mammographic images using the CS-reconstructed DBT images was similar to that of conventional mammographic images obtained using a full dose, indicating the efficacy of the proposed method. Consequently, we successfully reconstructed DBT images of substantially high quality using the CS-based algorithm and synthesised 2D mammographic images of reasonable quality, potentially reducing the radiation dose to patients in the complementary imaging technologies of DM and DBT.

Near Field Microwave Holography for Bio-Tissue Imaging

This study investigated the ability of microwave holography to accurately reconstruct the tissue structure of the human body. Numerical breast and head phantoms were imaged by 3D near-field holography using backscattered waves obtained by a monostatic planar scan. Complex organizational structures have been reconstructed accurately and quickly. In addition, breasts with relatively simple histology could be reconstructed without the matching liquid.

Compressive Sensing for Breast Microwave Imaging.

This work proposes a novel microwave imaging (MWI) multi-frequency technique, which combines compressive sensing (CS) with the well-known distorted Born iterative method (DBIM) to enhance the accuracy in the imaging procedure. CS strategies are emerging as a promising tool in MWI applications, which can also reduce the number of data samples. The proposed approach is based on an iterative shrinkage thresholding algorithm (ISTA), which has been modified to include an automatic and adaptive selection of multi-threshold values. The proposed implementation is applied in reconstruction of two-dimensional numerical heterogeneous breast phantoms, where it outer-performs the standard thresholding implementation and proves to be an interesting tool for medical imaging applications.

Towards Enhancing Skin Reflection Removal and Image Focusing Using a 3-D Breast Surface Reconstruction Algorithm

Suppressing skin reflection is vital for successful tumor detection in radar breast imaging systems. In this communication, a novel skin reflection removal (SRR) algorithm is presented based on a previously-proposed breast surface reconstruction algorithm. This skin reflection removal algorithm is validated using numerical MRI-derived breast models. This communication also investigates how the same skin location information can be used to enhance the delay-and-sum algorithm.

A method of truncated codifferential with application to some problems of cluster analysis

A method of truncated codifferential descent for minimizing continuously codifferentiable functions is suggested. The convergence of the method is studied. Results of numerical experiments are presented. Application of the suggested method for the solution of some problems of cluster analysis are discussed. In numerical experiments Wisconsin Diagnostic Breast Cancer database was used.