Digital Breast Tomosynthesis (DBT) offers potential insight into the fine details of
normal fibroglandular tissues and abnormal lesions, e.g., masses and micro-calcifications
associated with breast cancer, by the production of a pseudo-3D image. In addition, it
avoids the superposition, which is usually found in X-ray mammography, with a compa-
rable radiation dose. Algorithms to aid the human observer process DBT data sets involve
two key tasks: reconstruction and registration. In established medical image modalities
these tasks are normally performed sequentially; the images are reconstructed and then
registered. In this paper, we hypothesise that, for DBT in particular, combining the op-
timisation processes of reconstruction and registration into a single algorithm will offer
satisfactory for both tasks. Based on this hypothesis, we have devised a mathematical
framework to combine these two tasks, and have implemented both affine and non-linear
B-spline registration transformation models as plug-ins. By applying our algorithm to
various simulated data, we demonstrate the success of our method in terms of both re-
construction fidelity and in the registration accuracy of the recovered transformations.