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

Compressive sensing (CS) is an effective technique for reconstructing image from a small
amount of sampled data. It has been widely applied in medical imaging, remote sensing …

The family of sparse reconstruction techniques, including the recently introduced
compressed sensing framework, has been extensively explored to reduce scan times in …

Partial separability (PS) and sparsity have been previously used to enable reconstruction of
dynamic images from undersampled (k, t)-space data. This paper presents a new method to …

Purpose A 5D whole‐heart sparse imaging framework is proposed for simultaneous
assessment of myocardial function and high‐resolution cardiac and respiratory motion …

The reconstruction of dynamic magnetic resonance data from an undersampled k-space has
been shown to have a huge potential in accelerating the acquisition process of this imaging …

In this paper, we investigate a group sparse optimization problem via lp, q regularization in
three aspects: theory, algorithm and application. In the theoretical aspect, by introducing a …

Deep unfolding network (DUN) has become the mainstream for compressive sensing MRI
(CS-MRI) due to its good interpretability and high performance. Different optimization …

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

Purpose MR measurements from an echo‐planar imaging (EPI) sequence produce Nyquist
ghost artifacts that originate from inconsistencies between odd and even echoes. Several …