Abstract
| Original language | English |
|---|---|
| Pages (from-to) | 1-15 |
| Number of pages | 15 |
| Journal | Computer Methods in Biomechanics and Biomedical Engineering: Imagine and Visualisation |
| Volume | online |
| DOIs | |
| Publication status | Published - 2016 |
| Externally published | Yes |
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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 journal › Article
TY - JOUR
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
PY - 2016
Y1 - 2016
N2 - 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].
AB - 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].
KW - image registration
KW - knee kinematics
KW - magnetic resonance imaging
KW - compressed sensing
KW - Gaussian scale mixture
KW - total variation
KW - rapid MRI
KW - wavelet-based regularisation
U2 - 10.1080/21681163.2014.925304
DO - 10.1080/21681163.2014.925304
M3 - Article
VL - online
SP - 1
EP - 15
JO - Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization
JF - Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization
SN - 2168-1163
ER -