Deep learning is a class of machine learning methods that has been successful in computer
vision. Unlike traditional machine learning methods that require hand-engineered feature …

Compressed sensing (CS) and its medical applications are active areas of research. In this
paper, we review recent works using deep learning method to solve CS problem for images …

Magnetic resonance imaging (MRI) is one of the most important modalities for clinical
diagnosis. However, the main disadvantages of MRI are the long scanning time and the …

Low-Dose Computed Tomography (LDCT) technique, which reduces the radiation harm to
human bodies, is now attracting increasing interest in the medical imaging field. As the …

While enabling accelerated acquisition and improved reconstruction accuracy, current deep
MRI reconstruction networks are typically supervised, require fully sampled data, and are …

Accurately modeling and constructing a geologically realistic subsurface model remains an
outstanding problem as the morphology controls the flow behaviors. Particularly, one of the …

Lack of ground-truth MR images impedes the common supervised training of neural
networks for image reconstruction. To cope with this challenge, this article leverages …

Deep unfolding network (DUN) has become the mainstream for compressive sensing MRI
(CS-MRI) due to its good interpretability and high performance. Different optimization …

Background: The emergence of generative adversarial networks (GANs) has provided new
technology and framework for the application of medical images. Specifically, a GAN …

Fast magnetic resonance imaging (MRI) is crucial for clinical applications that can alleviate
motion artefacts and increase patient throughput. K-space undersampling is an obvious …