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A divide-and-conquer approach to compressed sensing MRI.
- Source :
-
Magnetic Resonance Imaging (0730725X) . Nov2019, Vol. 63, p37-48. 12p. - Publication Year :
- 2019
-
Abstract
- Compressed sensing (CS) theory assures us that we can accurately reconstruct magnetic resonance images using fewer k-space measurements than the Nyquist sampling rate requires. In traditional CS-MRI inversion methods, the fact that the energy within the Fourier measurement domain is distributed non-uniformly is often neglected during reconstruction. As a result, more densely sampled low frequency information tends to dominate penalization schemes for reconstructing MRI at the expense of high frequency details. In this paper, we propose a new framework for CS-MRI inversion in which we decompose the observed k-space data into "subspaces" via sets of filters in a lossless way, and reconstruct the images in these various spaces individually using off-the-shelf algorithms. We then fuse the results to obtain the final reconstruction. In this way, we are able to focus reconstruction on frequency information within the entire k-space more equally, preserving both high and low frequency details. We demonstrate that the proposed framework is competitive with state-of-the-art methods in CS-MRI in terms of quantitative performance, and often improves an algorithm's results qualitatively compared with its direct application to k-space. Unlabelled Image • The MRI measurements distribute non-uniformly in k-space. • A divide-and-conquer (DAC) approach for compressed sensing MRI is proposed. • The DAC approach can fully utilize the statistical characteristics of each k-space subspace. • The DAC approach often improves an undersampled MRI algorithm's results. [ABSTRACT FROM AUTHOR]
- Subjects :
- *COMPRESSED sensing
*MAGNETIC resonance imaging
Subjects
Details
- Language :
- English
- ISSN :
- 0730725X
- Volume :
- 63
- Database :
- Academic Search Index
- Journal :
- Magnetic Resonance Imaging (0730725X)
- Publication Type :
- Academic Journal
- Accession number :
- 139652061
- Full Text :
- https://doi.org/10.1016/j.mri.2019.06.014