This paper proposes a deep learning approach for accelerating magnetic resonance
imaging (MRI) using a large number of existing high quality MR images as the training …

Objective: Improve the reconstructed image with fast and multiclass dictionaries learning
when magnetic resonance imaging is accelerated by undersampling the k-space data …

Compressed sensing (CS) has exhibited great potential for accelerating magnetic
resonance imaging (MRI). In CS-MRI, we want to reconstruct a high-quality image from very …

In dynamic cardiac cine magnetic resonance imaging, the spatiotemporal resolution is
limited by the low imaging speed. Compressed sensing (CS) theory has been applied to …

To improve the compressive sensing MRI (CS-MRI) approaches in terms of fine structure
loss under high acceleration factors, we have proposed an iterative feature refinement …

Purpose Although recent deep learning methodologies have shown promising results in fast
MR imaging, how to explore it to learn an explicit prior and leverage it into the observation …

The progress of convolution neural network (CNN) based super-resolution has shown its
potential in image processing community. Meanwhile, Compressed Sensing MRI (CS-MRI) …

Deep learning, particularly the generative model, has demonstrated tremendous potential to
significantly speed up image reconstruction with reduced measurements recently. Rather …

Reducing scanning time is significantly important for MRI. Compressed sensing has shown
promising results by undersampling the k-space data to speed up imaging. Sparsity of an …

Decreasing magnetic resonance (MR) image acquisition times can potentially make MR
examinations more accessible. Prior arts including the deep learning models have been …