Conference proceeding
Blind compressed sensing with sparse dictionaries for accelerated dynamic MRI
2013 IEEE 10th International Symposium on Biomedical Imaging, pp.5-8
04/2013
DOI: 10.1109/ISBI.2013.6556398
PMCID: PMC3969877
PMID: 24691250
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.
Details
- Title: Subtitle
- Blind compressed sensing with sparse dictionaries for accelerated dynamic MRI
- Creators
- Sajan Goud Lingala - Dept. of Biomed. Eng., Univ. of Iowa, Iowa City, IA, USAMathews Jacob - Dept. of Electr. & Comput. Eng., Univ. of Iowa, Iowa City, IA, USA
- Resource Type
- Conference proceeding
- Publication Details
- 2013 IEEE 10th International Symposium on Biomedical Imaging, pp.5-8
- DOI
- 10.1109/ISBI.2013.6556398
- PMID
- 24691250
- PMCID
- PMC3969877
- NLM abbreviation
- Proc IEEE Int Symp Biomed Imaging
- ISSN
- 1945-7928
- eISSN
- 1945-8452
- Publisher
- IEEE
- Language
- English
- Date published
- 04/2013
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Iowa Neuroscience Institute; Radiation Oncology
- Record Identifier
- 9984070849102771
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