Toward a dose reduction strategy using model-based reconstruction with limited-angle tomosynthesis

Abstract

Model-based iterative reconstruction (MBIR) is an emerging technique for several imaging modalities and appli- cations including medical CT, security CT, PET, and microscopy. Its success derives from an ability to preserve image resolution and perceived diagnostic quality under impressively reduced signal level. MBIR typically uses a cost optimization framework that models system geometry, photon statistics, and prior knowledge of the recon- structed volume. The challenge of tomosynthetic geometries is that the inverse problem becomes more ill-posed due to the limited angles, meaning the volumetric image solution is not uniquely determined by the incom- pletely sampled projection data. Furthermore, low signal level conditions introduce additional challenges due to noise. A fundamental strength of MBIR for limited-views and limited-angle is that it provides a framework for constraining the solution consistent with prior knowledge of expected image characteristics. In this study, we analyze through simulation the capability of MBIR with respect to prior modeling components for limited-views, limited-angle digital breast tomosynthesis (DBT) under low dose conditions. A comparison to ground truth phantoms shows that MBIR with regularization achieves a higher level of fidelity and lower level of blurring and streaking artifacts compared to other state of the art iterative reconstructions, especially for high contrast objects. The benefit of contrast preservation along with less artifacts may lead to detectability improvement of microcalcification for more accurate cancer diagnosis.