Magnetic resonance imaging (MRI) is an inherently slow imaging modality, since it acquires
multi-dimensional k-space data through 1-D free induction decay or echo signals. This often …

Compressed sensing (CS) is a mathematical framework that reconstructs data from highly
undersampled measurements. To gain acceleration in acquisition time, CS has been …

Deep learning methods have achieved attractive performance in dynamic MR cine imaging.
However, most of these methods are driven only by the sparse prior of MR images, while the …

Dynamic MR image reconstruction from incomplete k‐space data has generated great
research interest due to its capability in reducing scan time. Nevertheless, the reconstruction …

High-dimensional MR imaging often requires long data acquisition time, thereby limiting its
practical applications. This paper presents a low-rank tensor based method for accelerated …

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 …

While many low rank and sparsity-based approaches have been developed for accelerated
dynamic magnetic resonance imaging (dMRI), they all use low rankness or sparsity in input …

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 …

T 1ρ relaxation time provides a new contrast mechanism that differs from T 1-and T 2-
weighted contrast, and is useful to study low-frequency motional processes and chemical …

Purpose To enable dynamic speech imaging with high spatiotemporal resolution and full‐
vocal‐tract spatial coverage, leveraging recent advances in sparse sampling. Methods An …