Compressed sensing MRI using sparsity induced from adjacent slice similarity

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 s...

Full description

Saved in:
Bibliographic Details
Published in2015 International Conference on Sampling Theory and Applications (SampTA) pp. 287 - 291
Main Authors Hirabayashi, A., Inamuro, N., Mimura, K., Kurihara, T., Homma, T.
Format Conference Proceeding
LanguageEnglish
Japanese
Published IEEE 01.05.2015
Subjects
Compressed sensing
Discrete Fourier transforms
Image coding
Image reconstruction
Magnetic resonance imaging
Sensors
Simulation
Online AccessGet full text
DOI10.1109/SAMPTA.2015.7148898

Cover

More Information
Summary: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.
DOI:10.1109/SAMPTA.2015.7148898