Dynamic contrast enhanced computed tomography (CT) is an imaging technique that
provides critical information on the relationship of vascular structure and dynamics in the …

Deep learning (DL) has been applied extensively in many computational imaging problems,
often leading to superior performance over traditional iterative approaches. However, two …

X-ray tomography of samples containing both weakly and strongly absorbing materials are
necessary in material and biomedical imaging. Extending the validity of the phase …

X-ray phase contrast imaging offers higher sensitivity compared to conventional X-ray
attenuation imaging and can be simply implemented by propagation when using a partially …

Deep neural networks have attracted growing interests in medical image due to its success
in computer vision tasks. One barrier for the application of deep neural networks is the need …

Deep neural networks (DNNs) are efficient solvers for ill-posed problems and have been
shown to outperform classical optimization techniques in several computational imaging …

X-ray phase contrast imaging holds great promise for improving the visibility of light-element
materials such as soft tissues and tumors. The single-mask differential phase contrast …

Computed tomography (CT) is a widely used imaging technique in both medical and
industrial applications. However, accurate CT reconstruction requires complete projection …

This paper introduces a deep learning network that reconstructs low-dose CT images into
CT images of a high quality comparable to adaptive statistical iterative reconstruction (ASIR) …

X-ray Computed Tomography (CT) imaging has been widely used in clinical diagnosis, non-
destructive examination, and public safety inspection. Sparse-view (sparse view) CT has …