Purpose The goal of this study is to develop a three‐dimensional (3D) iterative
reconstruction framework based on the deep learning (DL) technique to improve the digital …

Many useful image quality metrics for evaluating linear image reconstruction techniques do
not apply to or are difficult to interpret for nonlinear image reconstruction. The vast majority of …

In digital breast tomosynthesis (DBT) systems, projection data are acquired from a limited
number of angles. Consequently, the reconstructed images contain severe blurring artifacts …

The reconstruction of a volumetric image from Digital Breast Tomosynthesis (DBT)
measurements is an ill-posed inverse problem, for which existing iterative regularized …

Fiber-like features are an important aspect of breast imaging. Vessels and ducts are present
in all breast images, and spiculations radiating from a mass can indicate malignancy …

We propose a novel approach aiming to improve the detectability of microcalcifications in
Digital Breast Tomosynthesis (DBT) volumes. Hence, our contribution is twofold. First, we …

In this work we investigate an efficient implementation of a region-of-interest (ROI) based
Hotelling observer (HO) in the context of parameter optimization for detection of a rod signal …

Tomosynthesis offers an alternative to planar radiography providing pseudo-tomographic
information at a much lower radiation dose than CT. The fact that it cannot convey …

We characterize the detectability of fiber-like signals in digital breast tomosynthesis (DBT) for
linear iterative image reconstruction (IIR) algorithms. The detectability is investigated as a …

Digital breast tomosynthesis (DBT) has seen widespread clinical adoption for breast cancer
screening over the past decade as either a supplement to, or replacement for, digital …