  Digital breast tomosynthesis (DBT) is an emerging modality for breast
imaging. A typical tomosynthesis image is reconstructed from projection data
acquired at a limited number of views over a limited angular range. In general,
the quantitative accuracy of the image can be significantly compromised by
severe artifacts and non-isotropic resolution resulting from the incomplete
data. Nevertheless, it has been demonstrated that DBT may yield useful
information for detection/classification tasks and thus is considered a
promising breast imaging modality currently undergoing pre-clinical evaluation
trials. The purpose of this work is to conduct a preliminary, but systematic,
investigation and evaluation of the properties of reconstruction algorithms
that have been proposed for DBT. We use a breast phantom designed for DBT
evaluation to generate analytic projection data for a typical DBT
configuration, which is currently undergoing pre-clinical evaluation. The
reconstruction algorithms under comparison include (i) filtered backprojection
(FBP), (ii) expectation maximization (EM), and (iii) TV-minimization
algorithms. Results of our studies indicate that FBP reconstructed images are
generally noisier and demonstrate lower in-depth resolution than those obtained
through iterative reconstruction and that the TV-minimization reconstruction
yield images with reduced artifacts as compared to that obtained with other
algorithms under study.
