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PLoS One. 2013;8(2):e56098. doi: 10.1371/journal.pone.0056098. Epub 2013 Feb 8.

Interpolated compressed sensing for 2D multiple slice fast MR imaging.

Author information

1
Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, United States of America.

Abstract

Sparse MRI has been introduced to reduce the acquisition time and raw data size by undersampling the k-space data. However, the image quality, particularly the contrast to noise ratio (CNR), decreases with the undersampling rate. In this work, we proposed an interpolated Compressed Sensing (iCS) method to further enhance the imaging speed or reduce data size without significant sacrifice of image quality and CNR for multi-slice two-dimensional sparse MR imaging in humans. This method utilizes the k-space data of the neighboring slice in the multi-slice acquisition. The missing k-space data of a highly undersampled slice are estimated by using the raw data of its neighboring slice multiplied by a weighting function generated from low resolution full k-space reference images. In-vivo MR imaging in human feet has been used to investigate the feasibility and the performance of the proposed iCS method. The results show that by using the proposed iCS reconstruction method, the average image error can be reduced and the average CNR can be improved, compared with the conventional sparse MRI reconstruction at the same undersampling rate.

PMID:
23409130
PMCID:
PMC3568040
DOI:
10.1371/journal.pone.0056098
[Indexed for MEDLINE]
Free PMC Article

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