Compressed sensing MRI with singular value decomposition-based sparsity basis

Hong, Mingjian, Yu, Yeyang, Wang, Hua, Liu, Feng and Crozier, Stuart (2011) Compressed sensing MRI with singular value decomposition-based sparsity basis. Physics in Medicine and Biology, 56 19: 6311-6325. doi:10.1088/0031-9155/56/19/010


Author Hong, Mingjian
Yu, Yeyang
Wang, Hua
Liu, Feng
Crozier, Stuart
Title Compressed sensing MRI with singular value decomposition-based sparsity basis
Journal name Physics in Medicine and Biology   Check publisher's open access policy
ISSN 0031-9155
1361-6560
Publication date 2011-10
Sub-type Article (original research)
DOI 10.1088/0031-9155/56/19/010
Volume 56
Issue 19
Start page 6311
End page 6325
Total pages 15
Place of publication Bristol, England, U.K.
Publisher Institute of Physics Publishing
Collection year 2012
Language eng
Formatted abstract
Compressed sensing MRI (CS-MRI) aims to significantly reduce the measurements required for image reconstruction in order to accelerate the overall imaging speed. The sparsity of the MR images in transformation bases is one of the fundamental criteria for CS-MRI performance. Sparser representations can require fewer samples necessary for a successful reconstruction or achieve better reconstruction quality with a given number of samples. Generally, there are two kinds of 'sparsifying' transforms: predefined transforms and data-adaptive transforms. The predefined transforms, such as the discrete cosine transform, discrete wavelet transform and identity transform have usually been used to provide sufficiently sparse representations for limited types of MR images, in view of their isolation to the object images. In this paper, we present singular value decomposition (SVD) as the data-adaptive 'sparsity' basis, which can sparsify a broader range of MR images and perform effective image reconstruction. The performance of this method was evaluated for MR images with varying content (for example, brain images, angiograms, etc), in terms of image quality, reconstruction time, sparsity and data fidelity. Comparison with other commonly used sparsifying transforms shows that the proposed method can significantly accelerate the reconstruction process and still achieve better image quality, providing a simple and effective alternative solution in the CS-MRI framework.
Keyword Magnetic Resonance Imaging
Discrete cosine transforms
Image Reconstruction
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2012 Collection
School of Information Technology and Electrical Engineering Publications
 
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