期刊
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
卷 63, 期 9, 页码 1850-1861出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBME.2015.2503756
关键词
Compressed sensing (CS); dictionary learning; magnetic resonance imaging (MRI); sparse representation
资金
- NNSF of China [61571380, 61201045, 61302174, 11375147]
- Natural Science Foundation of Fujian Province of China [2015J01346]
- Fundamental Research Funds for the Central Universities [20720150109, 2013SH002]
- Important Joint Research Project on Major Diseases of Xiamen City [3502Z20149032]
- NSF [DMS-1418737]
- Direct For Mathematical & Physical Scien
- Division Of Mathematical Sciences [1418737] Funding Source: National Science Foundation
Objective: Improve the reconstructed image with fast and multiclass dictionaries learning when magnetic resonance imaging is accelerated by undersampling the k-space data. Methods: A fast orthogonal dictionary learning method is introduced into magnetic resonance image reconstruction to provide adaptive sparse representation of images. To enhance the sparsity, image is divided into classified patches according to the same geometrical direction and dictionary is trained within each class. A new sparse reconstruction model with the multiclass dictionaries is proposed and solved using a fast alternating direction method of multipliers. Results: Experiments on phantom and brain imaging data with acceleration factor up to 10 and various undersampling patterns are conducted. The proposed method is compared with state-of-the-art magnetic resonance image reconstruction methods. Conclusion: Artifacts are better suppressed and image edges are better preserved than the compared methods. Besides, the computation of the proposed approach is much faster than the typical K-SVD dictionary learning method in magnetic resonance image reconstruction. Significance: The proposed method can be exploited in undersampled magnetic resonance imaging to reduce data acquisition time and reconstruct images with better image quality.
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