Improvement of image performance in digital breast tomosynthesis (DBT) by incorporating a compressed-sensing (CS)-based deblurring scheme

Abstract In this work, we investigated a compressed-sensing (CS)-based deblurring scheme incorporated with the total-variation (TV) regularization penalty for image deblurring of high accuracy and adopted it into the image reconstruction in conventional digital breast tomosynthesis (DBT). We implemented the proposed algorithm and performed a systematic simulation to demonstrate its viability for improving the image performance in DBT as well as two-dimensional (2D) mammography. In the simulation, blurred noisy projection images of a 3D numerical breast phantom were generated by convolving their original (or exact ) version by a designed 2D Gaussian filter kernel (standard deviation=2 in pixel unit, kernel size=11×11), followed by adding Gaussian noise (mean=0, variance=0.05), and deblurred by using the algorithm before performing the DBT reconstruction procedure. Here the projection images were taken with a half tomographic angle of θ=20° and an angle step of Δθ=2°. We investigated the image performance of the reconstructed DBT images quantitatively in terms of the modulation and the slice-sensitive profile (SSP).