Magnetic resonance imaging (MRI) is renowned for its exceptional soft tissue contrast and
high spatial resolution, making it a pivotal tool in medical imaging. The integration of deep …

Magnetic resonance imaging (MRI) is one of the noninvasive imaging modalities that can
produce high-quality images. However, the scan procedure is relatively slow, which causes …

Following the success of deep learning in a wide range of applications, neural network-
based machine-learning techniques have received significant interest for accelerating …

This dissertation is devoted to provide advanced nonconvex nonsmooth variational model of
(Magnetic Resonance Image) MRI reconstruction, efficient learnable image reconstruction …

Artificial intelligence (AI) shows tremendous promise in the field of medical imaging, with
recent breakthroughs applying deep‐learning models for data acquisition, classification …

Image reconstruction from undersampled k-space data has been playing an important role
for fast MRI. Recently, deep learning has demonstrated tremendous success in various …

Artificial intelligence algorithms based on principles of deep learning (DL) have made a
large impact on the acquisition, reconstruction, and interpretation of MRI data. Despite the …

Abstract Magnetic Resonance Imaging (MRI) systems provide incredible soft tissue contrast
for the organ of interest in a non-invasive fashion. However, it is a notoriously slow imaging …

Deep learning (DL) has recently emerged as a pivotal technology for enhancing magnetic
resonance imaging (MRI), a critical tool in diagnostic radiology. This review paper provides …

UNIVERSITY OF CALIFORNIA Los Angeles Development of Deep Learning Methods for
Magnetic Resonance Imaging Reconstruction and An Page 1 UNIVERSITY OF …