Paper Info
Title
Combined Compressed Sensing And Parallel Mri Compared For Uniform And Random Cartesian Undersampling Of K-Space
Abstract
Both compressed sensing (CS) and parallel imaging effectively reconstruct magnetic resonance images from undersampled data. Combining both methods enables imaging with greater undersampling than accomplished previously. This paper investigates the choice of a suitable sampling pattern to accommodate both CS and parallel imaging. A combined method named SpRING is described and extended to handle random undersampling, and both GRAPPA and SpRING are evaluated for uniform and random undersampling using both simulated and real data. For the simulated data, when the undersampling factor is large, SpRING performs better with random undersampling. However, random undersampling is not as beneficial to SpRING for real data with approximate sparsity.
Year
DOI
Venue
2011
10.1109/ICASSP.2011.5946463
2011 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING
Keywords
Field
DocType
Compressed sensing, magnetic resonance imaging, image reconstruction, parallel imaging, sampling patterns
Kernel (linear algebra),Iterative reconstruction,Computer vision,k-space,Computer science,Undersampling,Oversampling and undersampling in data analysis,Sampling (statistics),Artificial intelligence,Compressed sensing,Cartesian coordinate system
Conference
ISSN
Citations 
PageRank 
1520-6149
2
0.46
References 
Authors
3
6
Name
Order
Citations
PageRank
Daniel S. Weller18814.85
Jonathan R. Polimeni297250.64
Leo Grady395556.83
Lawrence L. Wald461845.39
Elfar Adalsteinsson512916.18
Vivek K. Goyal62031171.16