Paper Info
Title
Compressed Sensing In Magnetic Resonance Imaging Using Non-Randomly Under-Sampled Signal In Cartesian Coordinates
Abstract
Image quality depends on the randomness of the k-space signal under-sampling in compressed sensing MRI (CS-MRI), especially for two-dimensional image acquisition. We investigate the feasibility of non-random signal under-sampling CS-MRI to stabilize the quality of re-constructed images and avoid arbitrariness in sampling point selection. Regular signal under-sampling for the phase-encoding direction is adopted, in which sampling points are chosen at equal intervals for the phase-encoding direction while varying the sampling density. Curvelet transform was adopted to remove the aliasing artifacts due to regular signal undersampling. To increase the incoherence between the measurement matrix and the sparsifying transform function, the scale of the curvelet transform was varied in each iterative image reconstruction step. We evaluated the obtained images by the peak-signal-to-noise ratio and root mean squared error in localized 3x3 pixel regions. Simulation studies and experiments showed that the signal-to-noise ratio and the structural similarity index of reconstructed images were comparable to standard random under-sampling CS. This study demonstrated the feasibility of non-random under-sampling based CS by using the multi-scale curvelet transform as a sparsifying transform function. The technique may help to stabilize the obtained image quality in CS-MRI.
Year
DOI
Venue
2019
10.1587/transinf.2019EDP7016
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
Keywords
Field
DocType
compressed sensing, curvelet transform, L1-norm, sparseness
Computer vision,Computer science,Artificial intelligence,Compressed sensing,Cartesian coordinate system,Magnetic resonance imaging
Journal
Volume
Issue
ISSN
E102D
9
1745-1361
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Ryo Kazama100.34
Kazuki Sekine200.34
Sadayoshi Ito3455.49