MR image reconstruction from undersampled data by using the iterative refinement procedure

Iterative feature refinement for accurate undersampled MR image reconstruction.

Accelerating MR scan is of great significance for clinical, research and advanced applications, and one main effort to achieve this is the utilization of compressed sensing (CS) theory. Nevertheless, the existing CSMRI approaches still have limitations such as fine structure loss or high computational complexity. This paper proposes a novel iterative feature refinement (IFR) module for accurate...

Undersampled MR Image Reconstruction with Data-Driven Tight Frame

Undersampled magnetic resonance image reconstruction employing sparsity regularization has fascinated many researchers in recent years under the support of compressed sensing theory. Nevertheless, most existing sparsity-regularized reconstruction methods either lack adaptability to capture the structure information or suffer from high computational load. With the aim of further improving image ...

Image Reconstruction from Incomplete Data

Image Reconstruction from Incomplete Data

Image Reconstruction from Undersampled Fourier Data Using the Polynomial Annihilation Transform

Fourier samples are collected in a variety of applications including magnetic resonance imaging (MRI) and synthetic aperture radar (SAR). The data are typically under-sampled and noisy. In recent years, l regularization has received considerable attention in designing image reconstruction algorithms from undersampled and noisy Fourier data. The underlying image is assumed to have some sparsity ...