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

Recent years have witnessed a significant advancement in brain imaging techniques that
offer a non-invasive approach to map** the structure and function of the brain …

Deep learning, due to its unprecedented success in tasks such as image classification, has
emerged as a new tool in image reconstruction with potential to change the field. In this …

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 …

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

Currently, the use of artificial intelligence (AI) in radiology, particularly machine learning
(ML), has become a reality in clinical practice. Since the end of the last century, several ML …

In compressed sensing MRI (CS-MRI), k-space measurements are under-sampled to
achieve accelerated scan times. CS-MRI presents two fundamental problems:(1) where to …

We propose a novel unsupervised deep-learning-based algorithm for dynamic magnetic
resonance imaging (MRI) reconstruction. Dynamic MRI requires rapid data acquisition for …