DocumentCode :
617245
Title :
Blind compressed sensing with sparse dictionaries for accelerated dynamic MRI
Author :
Lingala, Sajan Goud ; Jacob, Mathews
Author_Institution :
Dept. of Biomed. Eng., Univ. of Iowa, Iowa City, IA, USA
fYear :
2013
fDate :
7-11 April 2013
Firstpage :
5
Lastpage :
8
Abstract :
Several algorithms that model the voxel time series as a sparse linear combination of basis functions in a fixed dictionary were introduced to recover dynamic MRI data from under sampled Fourier measurements. We have recently demonstrated that the joint estimation of dictionary basis and the sparse coefficients from the k-space data results in improved reconstructions. In this paper, we investigate the use of additional priors on the learned basis functions. Specifically, we assume the basis functions to be sparse in pre-specified transform or operator domains. Our experiments show that this constraint enables the suppression of noisy basis functions, thus further improving the quality of the reconstructions. We demonstrate the usefulness of the proposed method through various reconstruction examples.
Keywords :
biomedical MRI; compressed sensing; image reconstruction; medical image processing; time series; accelerated dynamic MRI; basis functions; blind compressed sensing; joint estimation; k-space data; reconstruction; sampled Fourier measurements; sparse coefficients; sparse linear combination; voxel time series; Acceleration; Dictionaries; Image reconstruction; Magnetic resonance imaging; Noise; Noise measurement;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Biomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on
Conference_Location :
San Francisco, CA
ISSN :
1945-7928
Print_ISBN :
978-1-4673-6456-0
Type :
conf
DOI :
10.1109/ISBI.2013.6556398
Filename :
6556398
Link To Document :
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