The development of compressed sensing methods for magnetic resonance (MR) image
reconstruction led to an explosion of research on models and optimization algorithms for MR …

The development of compressed-sensing (CS) methods for magnetic resonance (MR)
image reconstruction led to an explosion of research on models and optimization algorithms …

This book presents a comprehensive review of convex optimization-based compressed
sensing magnetic resonance image reconstruction algorithms. Compressed sensing MRI …

For compressed sensing magnetic resonance imaging (CS-MRI) that utilize sparse
representations, the regularization parameter establishes a trade-off between sparsity and …

Compressed sensing techniques allow MRI reconstruction from undersampled k-space
data. However, most reconstruction methods suffer from high computational costs, selection …

The articles in this special section focus on computational magnetic resonance imaging
(MRI) using compressed sensing applications. Presents recent developments in …

Expecting the reader to have some basic training in liner algebra and optimization, the book
begins with a general discussion on CS techniques and algorithms. It moves on to …

Introduction Applying compressed sensing (CS)[1, 2] in MRI has recently attracted much
attention, and initial investigation has shown that MR images can be reconstructed from a …

Compressed sensing (CS) enables the reconstruction of MR images from highly under-
sampled k-space data via a constrained ℓ 1-minimization problem. However, existing …

Both L1 minimization [1] and homotopic L0 minimization [2] techniques have shown success
in compressed-sensing MRI reconstruction using reduced k-space data. L1 minimization …