  The two-dimensional nature of mammography makes estimation of the overall
breast density challenging, and estimation of the true patient-specific
radiation dose impossible. Digital breast tomosynthesis (DBT), a pseudo-3D
technique, is now commonly used in breast cancer screening and diagnostics.
Still, the severely limited 3rd dimension information in DBT has not been used,
until now, to estimate the true breast density or the patient-specific dose.
This study proposes a reconstruction algorithm for DBT based on deep learning
specifically optimized for these tasks. The algorithm, which we name DBToR, is
based on unrolling a proximal-dual optimization method. The proximal operators
are replaced with convolutional neural networks and prior knowledge is included
in the model. This extends previous work on a deep learning-based
reconstruction model by providing both the primal and the dual blocks with
breast thickness information, which is available in DBT. Training and testing
of the model were performed using virtual patient phantoms from two different
sources. Reconstruction performance, and accuracy in estimation of breast
density and radiation dose, were estimated, showing high accuracy (density
<+/-3%; dose <+/-20%) without bias, significantly improving on the current
state-of-the-art. This work also lays the groundwork for developing a deep
learning-based reconstruction algorithm for the task of image interpretation by
radiologists.
