Estimating the efficiency of the simultaneous algebraic reconstruction technique (SART), Bayesian inference reconstruction (BIR), and traditional shift-and-add (SAA) tomosynthesis using medical phantoms

Abstract

New parallel iteration algorithms that provide real-time reconstruction of the 3D breast images restored from an incomplete set of noisy mammograms are studied. The simultaneous algebraic reconstruction technique (SART) and Bayesian inference reconstruction (BIR) are considered as advantageous iteration methods that are most suitable for improving the quality of the reconstructed 3D images. The graphics processing unit (GPU) is used to accelerate the reconstruction. The minimization of total variation (TV) is used as a priori support for the regularization of the iteration process and decrease of the noise level in the reconstructed images. Preliminary results for medical physical phantoms show that all the methods are sufficient for the layer-by-layer reconstruction of medical model objects and separation of layers whose images are overlapped on a mammogram that corresponds to vertical transmission (direction along the OZ axis). The traditional shift-and-add (SAA) tomosynthesis is established to be less efficient than SART and BIR in terms of the anatomical-noise reduction and blurring of reconstructed 3D images between conjugate layers. Despite the fact that the estimated contrast-noise ratio, given internal structures with low contrast, is higher for SAA as compared to SART and BIR, its efficiency is very low given the highly structured background. In our opinion, optimal results can be achieved using BIR.