Exploiting sparsity and low-rank structure for the recovery of multi-slice breast MRIs with reduced sampling error.
It has been shown that, magnetic resonance images (MRIs) with sparsity representation in a transformed domain, e.g. spatial finite-differences (FD), or discrete cosine transform (DCT), can be restored from undersampled k-space via applying current compressive sampling theory. The paper presents a mo...
| Published in: | Medical & Biological Engineering & Computing Vol. 50; no. 9; pp. 991 - 1001 |
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| Main Authors: | , , , , , , , , , |
| Format: | research Journal Article |
| Published: |
Springer Nature
Sep2012
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104368725&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104368725 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Sep2012 vid: 50 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104368725 NLM22644257 2011689589 10.1007/s11517-012-0920-x NLM22644257 104368725 ppf: 991 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Exploiting sparsity and low-rank structure for the recovery of multi-slice breast MRIs with reduced sampling error. aug: au: Yin XX Ng BW Ramamohanarao K Baghai-Wadji A Abbott D Yin, X X Ng, B W-H Ramamohanarao, K Baghai-Wadji, A Abbott, D affil: Centre for Biomedical Engineering, School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, SA, Australia sug: subj: Algorithms Artifacts Breast Neoplasms Pathology Image Enhancement Methods Image Interpretation, Computer Assisted Methods Magnetic Resonance Imaging Methods Information Science Methods Female Human Reproducibility of Results Sample Size Sensitivity and Specificity Female ab: It has been shown that, magnetic resonance images (MRIs) with sparsity representation in a transformed domain, e.g. spatial finite-differences (FD), or discrete cosine transform (DCT), can be restored from undersampled k-space via applying current compressive sampling theory. The paper presents a model-based method for the restoration of MRIs. The reduced-order model, in which a full-system-response is projected onto a subspace of lower dimensionality, has been used to accelerate image reconstruction by reducing the size of the involved linear system. In this paper, the singular value threshold (SVT) technique is applied as a denoising scheme to reduce and select the model order of the inverse Fourier transform image, and to restore multi-slice breast MRIs that have been compressively sampled in k-space. The restored MRIs with SVT for denoising show reduced sampling errors compared to the direct MRI restoration methods via spatial FD, or DCT. Compressive sampling is a technique for finding sparse solutions to underdetermined linear systems. The sparsity that is implicit in MRIs is to explore the solution to MRI reconstruction after transformation from significantly undersampled k-space. The challenge, however, is that, since some incoherent artifacts result from the random undersampling, noise-like interference is added to the image with sparse representation. These recovery algorithms in the literature are not capable of fully removing the artifacts. It is necessary to introduce a denoising procedure to improve the quality of image recovery. This paper applies a singular value threshold algorithm to reduce the model order of image basis functions, which allows further improvement of the quality of image reconstruction with removal of noise artifacts. The principle of the denoising scheme is to reconstruct the sparse MRI matrices optimally with a lower rank via selecting smaller number of dominant singular values. The singular value threshold algorithm is performed by minimizing the nuclear norm of difference between the sampled image and the recovered image. It has been illustrated that this algorithm improves the ability of previous image reconstruction algorithms to remove noise artifacts while significantly improving the quality of MRI recovery. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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