SMS-HSL: Simultaneous multislice aliasing separation exploiting hankel subspace learning.

Abstract:

PURPOSE:To develop a novel, simultaneous multislice reconstruction method that exploits Hankel subspace learning (SMS-HSL) for aliasing separation in the slice direction. METHODS:An SMS signal model with the Hankel-structured matrix was proposed. To efficiently suppress interslice leakage artifacts from a signal subspace perspective, a null space was learned from the reference data combined over all slices other than a slice of interest using singular value decomposition. Given the fact that the Hankel-structured matrix is rank-deficient while the magnitude between the reference and its estimate is similar in k-space, the SMS-HSL was reformulated as a constrained optimization problem with both low-rank and magnitude priors. SMS signals were projected onto a slice-specific subspace while undesired signals were eliminated using the null space operator. The simulations and experiments were performed with increasing multiband factors up to 6 using the SMS-HSL and the split slice-GRAPPA. RESULTS:Compared with the split slice-GRAPPA, the SMS-HSL shows superior performance in suppressing aliasing artifacts and noises at high multiband factors even with: insufficient reference signals, a small number of coils, and a short distance between aliasing voxels. CONCLUSION:We successfully demonstrated the effectiveness of the SMS-HSL over the split slice-GRAPPA for aliasing separation in the slice direction. Magn Reson Med 78:1392-1404, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

journal_name

Magn Reson Med

authors

Park S,Park J

doi

10.1002/mrm.26527

subject

Has Abstract

pub_date

2017-10-01 00:00:00

pages

1392-1404

issue

4

eissn

0740-3194

issn

1522-2594

journal_volume

78

pub_type

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