Improved regularisation constraints for compressed sensing of multi-slice MRI

Rafiqul Islam, Andrew Lambert, Mark Pickering, Jennie Scarvell, Paul, N Smith

Research output: Contribution to journalArticle

Abstract

In magnetic resonance imaging, the long acquisition time required to capture k-space data according to the Nyquist sampling rule is a major limitation. Methods for reducing the scan time for these types of imaging procedures have attracted considerable research interest. Compressed sensing approaches have recently been applied to allow faster acquisition by undersampling the k-space data. However, random undersampling introduces noise-like artefacts. To address this issue, a number of nonlinear reconstruction methods have been proposed that use norm regularisation with a sparsifying transform. In this paper, we present a reconstruction method in which a Gaussian scale mixture model constraint in the wavelet domain is combined with a total variation constraint for use as a regularisation prior. A series of experimental evaluations are conducted to validate our method using synthetic and real multi-slice MRI data for the purposes of faster acquisition. Our results show that the volume reconstructed by ou Improved regularisation constraints for compressed sensing of multi-slice MRI - ResearchGate. Available from: http://www.researchgate.net/publication/263280801_Improved_regularisation_constraints_for_compressed_sensing_of_multi-slice_MRI [accessed Apr 23, 2015].
Original languageEnglish
Pages (from-to)1-15
Number of pages15
JournalComputer Methods in Biomechanics and Biomedical Engineering: Imagine and Visualisation
Volumeonline
DOIs
Publication statusPublished - 2016
Externally publishedYes

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Compressed sensing
Magnetic resonance imaging
Imaging techniques
Magnetic resonance
Sampling
Artifacts
Noise
Publications
Magnetic Resonance Imaging
Research

Cite this

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title = "Improved regularisation constraints for compressed sensing of multi-slice MRI",
abstract = "In magnetic resonance imaging, the long acquisition time required to capture k-space data according to the Nyquist sampling rule is a major limitation. Methods for reducing the scan time for these types of imaging procedures have attracted considerable research interest. Compressed sensing approaches have recently been applied to allow faster acquisition by undersampling the k-space data. However, random undersampling introduces noise-like artefacts. To address this issue, a number of nonlinear reconstruction methods have been proposed that use norm regularisation with a sparsifying transform. In this paper, we present a reconstruction method in which a Gaussian scale mixture model constraint in the wavelet domain is combined with a total variation constraint for use as a regularisation prior. A series of experimental evaluations are conducted to validate our method using synthetic and real multi-slice MRI data for the purposes of faster acquisition. Our results show that the volume reconstructed by ou Improved regularisation constraints for compressed sensing of multi-slice MRI - ResearchGate. Available from: http://www.researchgate.net/publication/263280801_Improved_regularisation_constraints_for_compressed_sensing_of_multi-slice_MRI [accessed Apr 23, 2015].",
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Improved regularisation constraints for compressed sensing of multi-slice MRI. / Islam, Rafiqul; Lambert, Andrew; Pickering, Mark; Scarvell, Jennie; Smith, Paul, N.

In: Computer Methods in Biomechanics and Biomedical Engineering: Imagine and Visualisation, Vol. online, 2016, p. 1-15.

Research output: Contribution to journalArticle

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T1 - Improved regularisation constraints for compressed sensing of multi-slice MRI

AU - Islam, Rafiqul

AU - Lambert, Andrew

AU - Pickering, Mark

AU - Scarvell, Jennie

AU - Smith, Paul, N

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