Medical imaging is playing a more and more important role in clinics. However, there are
several issues in different imaging modalities such as slow imaging speed in MRI, radiation …

Image reconstruction from computed tomography measurement is formulated as a thought-
provoking statistical inverse problem. Deep learning algorithms are best for ill-posed …

Computerized tomography (CT) is widely used for clinical screening and treatment planning.
In this study, we aimed to reduce X‐ray radiation and achieve high‐quality CT imaging by …

Purpose The reconstruction performance of the deep image prior (DIP) approach is limited
by the conventional convolutional layer structure and it is difficult to enhance its potential. In …

The current mainstream computed tomography (CT) reconstruction methods based on deep
learning usually need to fix the scanning geometry and dose level, which significantly …

In recent years, computed tomography (CT) has been widely used in various clinical
diagnosis. Given potential health risks bring by the X-ray radiation, the major objective of the …

Objective The increasing number of coronary computed tomography angiography (CCTA)
requests raised concerns about dose exposure. New dose reduction strategies based on …

Background The widespread application of X-ray computed tomography (CT) imaging in
medical screening makes radiation safety a major concern for public health. Sparse-view CT …

Deep learning (DL) is widely used for MRI reconstruction and leverages significant
promotion. However, the existing DL-based methods still have some weaknesses. First, the …

Chest X-ray (CXR) is becoming a useful method in the evaluation of coronavirus disease 19
(COVID-19). Despite the global spread of COVID-19, utilizing a computer-aided diagnosis …