Bayesian reconstruction for digital breast tomosynthesis using a non-local Gaussian Markov random field a priori model

Abstract

Noise is an intrinsic property of every imaging system. For imaging systems using ionizing radiation, such as digital breast tomosynthesis (DBT) or digital mammography (DM), we strive to ensure that x-ray quantum noise is the limiting noise source in images, while using the lowest radiation dose possible to achieve clinically satisfactory images. Therefore, new computer methods are being sought to help reduce the dose of these systems. In the case of DBT, this can be achieved when solving the inverse problem of tomographic reconstruction. In this work, we propose to use a Non-Local Gaussian Markov Random Field (NLGMRF) model to represent a priori knowledge in a Bayesian (Maximum a Posteriori - MAP) reconstruction approach for DBT. The main advantage of the Non-Local Markov Random Field models is that they explicitly consider two important constraints to regularize the solution of this inverse problem - smoothing and redundancy. To evaluate this new method in DBT, a number of experiments were performed to compare these methods to existing reconstruction techniques. Comparable or superior results were achieved when compared with methods in the DBT reconstruction literature in terms of structural similarity index (SSIM), artifact spread function (ASF) and visual analysis, demonstrating that the NLGMRF model is suitable to regularize the MAP solution in DBT reconstruction.