Generative adversarial networks have gained a lot of attention in the computer vision
community due to their capability of data generation without explicitly modelling the …

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

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 …

Purpose To develop a strategy for training a physics‐guided MRI reconstruction neural
network without a database of fully sampled data sets. Methods Self‐supervised learning via …

Although deep learning models like CNNs have achieved great success in medical image
analysis, the small size of medical datasets remains a major bottleneck in this area. To …

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 …

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 …

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 …