  Purpose: The goal of this study is to develop a novel deep learning (DL)
based reconstruction framework to improve the digital breast tomosynthesis
(DBT) imaging performance. Methods: In this work, the DIR-DBTnet is developed
for DBT image reconstruction by unrolling the standard iterative reconstruction
algorithm within the deep learning framework. In particular, such network
learns the regularizer and the iteration parameters automatically through
network training with a large amount of simulated DBT data. Afterwards, both
numerical and experimental data are used to evaluate its performance.
Quantitative metrics such as the artifact spread function (ASF), breast
density, and the signal difference to noise ratio (SDNR) are used for image
quality assessment. Results: For both numerical and experimental data, the
proposed DIR-DBTnet generates reduced in-plane shadow artifacts and
out-of-plane artifacts compared with the filtered back projection (FBP) and
total variation (TV) methods. Quantitatively, the full width half maximum
(FWHM) of the measured ASF curve from the numerical data is 33.4% and 19.7%
smaller than those obtained with the FBP and TV methods, respectively; the
breast density of the network reconstructed DBT images is more accurate and
consistent with the ground truth. Conclusions: In conclusion, a deep iterative
reconstruction network, DIR-DBTnet, has been proposed. Both qualitative and
quantitative analyses of the numerical and experimental results show superior
DBT imaging performance than the FBP and iterative algorithms.
