DocumentCode :
719284
Title :
Compressed sensing MRI using sparsity induced from adjacent slice similarity
Author :
Hirabayashi, A. ; Inamuro, N. ; Mimura, K. ; Kurihara, T. ; Homma, T.
Author_Institution :
Coll. of Inf., Sci. & Eng., Ritsumeikan Univ., Kusatsu, Japan
fYear :
2015
fDate :
25-29 May 2015
Firstpage :
287
Lastpage :
291
Abstract :
We propose a fast magnetic resonance imaging (MRI) technique based on compressed sensing. The main idea is to use a combination of full and compressed sensing. Full sensing is conducted for every several slices (F-slice) while compressed sensing with high compression rate is applied to the rest of slices (C-slice). We can perfectly reconstruct F-slice images, which are used to roughly estimate the C-slices. Since the estimate is already of good quality, its difference from the original image is small and sparse. Therefore, the difference can be reconstructed precisely using the standard compressed sensing technique even with high compression rate. Simulation results show that the proposed method outperforms conventional methods with 3.16dB for arm images, 0.26dB for brain images in average for the C-slices with perfect reconstruction for the F-slices.
Keywords :
biomedical MRI; brain; compressed sensing; image reconstruction; medical image processing; C-slice; F-slice image reconstruction; adjacent slice similarity; arm images; brain images; compressed sensing MRI; compression rate; full sensing; magnetic resonance imaging technique; original image; sparsity; standard compressed sensing technique; Compressed sensing; Discrete Fourier transforms; Image coding; Image reconstruction; Magnetic resonance imaging; Sensors; Simulation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Sampling Theory and Applications (SampTA), 2015 International Conference on
Conference_Location :
Washington, DC
Type :
conf
DOI :
10.1109/SAMPTA.2015.7148898
Filename :
7148898
Link To Document :
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