Magnetic Resonance Imaging (MRI) is an essential medical imaging modality that provides
excellent soft-tissue contrast and high-resolution images of the human body, allowing us to …

Multiparametric magnetic resonance imaging (mpMRI) is an indispensable tool in the
clinical workflow for the diagnosis and treatment planning of various diseases. Machine …

Multi-institutional efforts can facilitate training of deep MRI reconstruction models, albeit
privacy risks arise during cross-site sharing of imaging data. Federated learning (FL) has …

Abstract Multi-contrast MRI (MC-MRI) captures multiple complementary imaging modalities
to aid in radiological decision-making. Given the need for lowering the time cost of multiple …

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

The long acquisition time has limited the accessibility of magnetic resonance imaging (MRI)
because it leads to patient discomfort and motion artifacts. Although several MRI techniques …

Deep learning (DL) in magnetic resonance imaging (MRI) shows excellent performance in
image reconstruction from undersampled k-space data. Artifact-free and high-quality MRI …

Reconstruction methods based on deep learning have greatly shortened the data
acquisition time of magnetic resonance imaging (MRI). However, these methods typically …

In recent years, there has been attention on leveraging the statistical modeling capabilities
of neural networks for reconstructing sub-sampled Magnetic Resonance Imaging (MRI) data …

In cardiac CINE, motion-compensated MR reconstruction (MCMR) is an effective approach
to address highly undersampled acquisitions by incorporating motion information between …