Compressed sensing (CS) has been playing a key role in accelerating the magnetic
resonance imaging (MRI) acquisition process. With the resurgence of artificial intelligence …

The performance of artificial intelligence (AI) for brain MRI can improve if enough data are
made available. Generative adversarial networks (GANs) showed a lot of potential to …

Deep MRI reconstruction is commonly performed with conditional models that de-alias
undersampled acquisitions to recover images consistent with fully-sampled data. Since …

Supervised reconstruction models are characteristically trained on matched pairs of
undersampled and fully-sampled data to capture an MRI prior, along with supervision …

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 …

Accelerated multi-modal magnetic resonance (MR) imaging is a new and effective solution
for fast MR imaging, providing superior performance in restoring the target modality from its …

Multi-contrast MRI protocols increase the level of morphological information available for
diagnosis. Yet, the number and quality of contrasts are limited in practice by various factors …

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 …

The accelerating magnetic resonance imaging (MRI) reconstruction process is a challenging
ill-posed inverse problem due to the excessive under-sampling operation in-space. In this …

Magnetic Resonance Imaging (MRI) has long been considered to be among" the gold
standards" of diagnostic medical imaging. The long acquisition times, however, render MRI …