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

Cardiac cine magnetic resonance imaging (MRI) reconstruction is challenging due to spatial
and temporal resolution trade-offs. Temporal correlation in cardiac cine MRI is informative …

Magnetic resonance imaging (MRI) is one of the most important modalities for clinical
diagnosis. However, the main disadvantages of MRI are the long scanning time and the …

Adaptive intelligence aims at empowering machine learning techniques with the additional
use of domain knowledge. In this work, we present the application of adaptive intelligence to …

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

Abstract The application of Magnetic Resonance Imaging (MRI) is limited due to the long
acquisition time of k-space signals. Recently, many deep learning-based MR image …

Meta-learning has recently been an emerging data-efficient learning technique for various
medical imaging operations and has helped advance contemporary deep learning models …

This paper presents a novel multi-pooling architecture generated by combining the
advantages of wavelet and max-pooling operations in convolutional neural networks …

Abstract Background and Objective Background and Objective: Recent studies in deep
learning reveal that the U-Net stands out among the diverse set of deep models as an …

Objective To improve accelerated MRI reconstruction through a densely connected
cascading deep learning reconstruction framework. Materials and methods A cascading …