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Sparse BLIP: BLind Iterative Parallel imaging reconstruction using compressed sensing.
Magn Reson Med. 2014 Feb; 71(2):645-60.MR

Abstract

PURPOSE

To develop a sensitivity-based parallel imaging reconstruction method to reconstruct iteratively both the coil sensitivities and MR image simultaneously based on their prior information.

METHODS

Parallel magnetic resonance imaging reconstruction problem can be formulated as a multichannel sampling problem where solutions are sought analytically. However, the channel functions given by the coil sensitivities in parallel imaging are not known exactly and the estimation error usually leads to artifacts. In this study, we propose a new reconstruction algorithm, termed Sparse BLind Iterative Parallel, for blind iterative parallel imaging reconstruction using compressed sensing. The proposed algorithm reconstructs both the sensitivity functions and the image simultaneously from undersampled data. It enforces the sparseness constraint in the image as done in compressed sensing, but is different from compressed sensing in that the sensing matrix is unknown and additional constraint is enforced on the sensitivities as well. Both phantom and in vivo imaging experiments were carried out with retrospective undersampling to evaluate the performance of the proposed method.

RESULTS

Experiments show improvement in Sparse BLind Iterative Parallel reconstruction when compared with Sparse SENSE, JSENSE, IRGN-TV, and L1-SPIRiT reconstructions with the same number of measurements.

CONCLUSION

The proposed Sparse BLind Iterative Parallel algorithm reduces the reconstruction errors when compared to the state-of-the-art parallel imaging methods.

Authors+Show Affiliations

Department of Electrical and Computer Engineering, University of Utah, Salt Lake City, Utah, USA.No affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Comparative Study
Evaluation Study
Journal Article
Research Support, U.S. Gov't, Non-P.H.S.

Language

eng

PubMed ID

23508781

Citation

She, Huajun, et al. "Sparse BLIP: BLind Iterative Parallel Imaging Reconstruction Using Compressed Sensing." Magnetic Resonance in Medicine, vol. 71, no. 2, 2014, pp. 645-60.
She H, Chen RR, Liang D, et al. Sparse BLIP: BLind Iterative Parallel imaging reconstruction using compressed sensing. Magn Reson Med. 2014;71(2):645-60.
She, H., Chen, R. R., Liang, D., DiBella, E. V., & Ying, L. (2014). Sparse BLIP: BLind Iterative Parallel imaging reconstruction using compressed sensing. Magnetic Resonance in Medicine, 71(2), 645-60. https://doi.org/10.1002/mrm.24716
She H, et al. Sparse BLIP: BLind Iterative Parallel Imaging Reconstruction Using Compressed Sensing. Magn Reson Med. 2014;71(2):645-60. PubMed PMID: 23508781.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Sparse BLIP: BLind Iterative Parallel imaging reconstruction using compressed sensing. AU - She,Huajun, AU - Chen,Rong-Rong, AU - Liang,Dong, AU - DiBella,Edward V R, AU - Ying,Leslie, PY - 2013/3/20/entrez PY - 2013/3/20/pubmed PY - 2015/10/10/medline SP - 645 EP - 60 JF - Magnetic resonance in medicine JO - Magn Reson Med VL - 71 IS - 2 N2 - PURPOSE: To develop a sensitivity-based parallel imaging reconstruction method to reconstruct iteratively both the coil sensitivities and MR image simultaneously based on their prior information. METHODS: Parallel magnetic resonance imaging reconstruction problem can be formulated as a multichannel sampling problem where solutions are sought analytically. However, the channel functions given by the coil sensitivities in parallel imaging are not known exactly and the estimation error usually leads to artifacts. In this study, we propose a new reconstruction algorithm, termed Sparse BLind Iterative Parallel, for blind iterative parallel imaging reconstruction using compressed sensing. The proposed algorithm reconstructs both the sensitivity functions and the image simultaneously from undersampled data. It enforces the sparseness constraint in the image as done in compressed sensing, but is different from compressed sensing in that the sensing matrix is unknown and additional constraint is enforced on the sensitivities as well. Both phantom and in vivo imaging experiments were carried out with retrospective undersampling to evaluate the performance of the proposed method. RESULTS: Experiments show improvement in Sparse BLind Iterative Parallel reconstruction when compared with Sparse SENSE, JSENSE, IRGN-TV, and L1-SPIRiT reconstructions with the same number of measurements. CONCLUSION: The proposed Sparse BLind Iterative Parallel algorithm reduces the reconstruction errors when compared to the state-of-the-art parallel imaging methods. SN - 1522-2594 UR - https://www.unboundmedicine.com/medline/citation/23508781/Sparse_BLIP:_BLind_Iterative_Parallel_imaging_reconstruction_using_compressed_sensing_ L2 - https://doi.org/10.1002/mrm.24716 DB - PRIME DP - Unbound Medicine ER -