MRI Reconstruction From Sparse K-Space Data Using Low Dimensional Manifold Model

MRI Reconstruction From Sparse K-Space Data Using Low Dimensional Manifold Model

Recent compressed sensing (CS) approaches to utilize the similarity and redundancy of magnetic resonance (MR) image patches to enable reconstruction from sparse k-space measurements. In this paper, the patches' similarity and redundancy are exploited by applying the low-dimensional manifold mod...

Full description

Bibliographic Details
Main Authors: Saim Abdullah, Omar Arif, M. Bilal Arif, Tahir Mahmood
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
MRI reconstruction
sparse recovery
compressed sensing
manifold learning
Online Access:https://ieeexplore.ieee.org/document/8746105/

Internet

https://ieeexplore.ieee.org/document/8746105/

Similar Items