Digital Breast Tomosynthesis (DBT) provides an insight into the fine details of normal
fibroglandular tissues and abnormal lesions by reconstructing a pseudo-3D image of the
breast. In this respect, DBT overcomes a major limitation of conventional X-ray mam-
mography by reducing the confounding effects caused by the superposition of breast
tissue. In a breast cancer screening or diagnostic context, a radiologist is interested in
detecting change, which might be indicative of malignant disease. To help automate
this task image registration is required to establish spatial correspondence between time
points. Typically, images, such as MRI or CT, are first reconstructed and then registered.
This approach can be effective if reconstructing using a complete set of data. However,
for ill-posed, limited-angle problems such as DBT, estimating the deformation is com-
plicated by the significant artefacts associated with the reconstruction, leading to severe
inaccuracies in the registration.
This paper presents a mathematical framework, which couples the two tasks and
jointly estimates both image intensities and the parameters of a transformation. Under
this framework, we compare an iterative method and a simultaneous method, both of
which tackle the problem of comparing DBT data by combining reconstruction of a pair
of temporal volumes with their registration.
We evaluate our methods using various computational digital phantoms, uncom-
pressed breast MR images, and in-vivo DBT simulations. Firstly, we compare both iter-
ative and simultaneous methods to the conventional, sequential method using an affine
transformation model. We show that jointly estimating image intensities and parametric
transformations gives superior results with respect to reconstruction fidelity and regis-
tration accuracy. Also, we incorporate a non-rigid B-spline transformation model into
our simultaneous method. The results demonstrate a visually plausible recovery of the
deformation with preservation of the reconstruction fidelity.