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

Suhyung Park, Jaeseok Park

Research output: Contribution to journalArticle

3 Citations (Scopus)

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.

Original languageEnglish
JournalMagnetic Resonance in Medicine
DOIs
Publication statusAccepted/In press - 2016
Externally publishedYes

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Artifacts
Learning
Noise
Direction compound

Keywords

  • Hankel matrix
  • Low rank
  • Magnetic resonance imaging
  • Parallel imaging
  • Simultaneous multislice

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

Cite this

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title = "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.",
keywords = "Hankel matrix, Low rank, Magnetic resonance imaging, Parallel imaging, Simultaneous multislice",
author = "Suhyung Park and Jaeseok Park",
year = "2016",
doi = "10.1002/mrm.26527",
language = "English",
journal = "Magnetic Resonance in Medicine",
issn = "0740-3194",
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AB - 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.

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