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

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

In low dose computed tomography (LDCT) imaging, the data inconsistency of measured
noisy projections can significantly deteriorate reconstruction images. To deal with this …

Three-dimensional MRI has gained increasing popularity in various clinical applications due
to its improved through-plane spatial resolution, which enhances the detection of subtle …

Purpose To evaluate the feasibility of accelerated chemical‐exchange‐saturation‐transfer
(CEST) imaging using a combination of compressed sensing (CS) and sensitivity encoding …

Several deep‐learning models have been proposed to shorten MRI scan time. Prior deep‐
learning models that utilize real‐valued kernels have limited capability to learn rich …

Dynamic magnetic resonance imaging (MRI) acquisitions are relatively slow due to physical
and physiological limitations. The spatial-temporal dictionary learning (DL) approach …

Undersampling-space data in magnetic resonance imaging (MRI) reduces scan time but
pose challenges in image reconstruction. Considerable progress has been made in …

This paper proposes a concise and effective approach termed discriminative feature
representation (DFR) for low dose computerized tomography (LDCT) image processing …

Purpose Long scanning time greatly hinders the widespread application of spin‐lattice
relaxation in rotating frame (T 1 ρ) in clinics. In this study, a novel method is proposed to …