A singular K-space model for fast reconstruction of magnetic resonance images from undersampled data.
Reconstructing magnetic resonance images from undersampled k-space data is a challenging problem. This paper introduces a novel method of image reconstruction from undersampled k-space data based on the concept of singularizing operators and a novel singular k-space model. Exploring the sparsity of...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 56; no. 7; pp. 1211 - 1226 |
|---|---|
| Autores principales: | , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Jul2018
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=130320746&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 130320746 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jul2018 vid: 56 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 130320746 130320746 NLM29222614 10.1007/s11517-017-1763-2 NLM29222614 130320746 ppf: 1211 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A singular K-space model for fast reconstruction of magnetic resonance images from undersampled data. aug: au: Luo, Jianhua Mou, Zhiying Qin, Binjie Li, Wanqing Ogunbona, Philip Robini, Marc C. Zhu, Yuemin affil: School of Aeronautics and Astronautics, Shanghai Jiao Tong University, 200240, Shanghai, People’s Republic of China sug: subj: Algorithms Image Processing, Computer Assisted Models, Theoretical Magnetic Resonance Imaging Sensitivity and Specificity Elasticity Phantoms, Imaging Clinical Assessment Tools Ferrans and Powers Quality of Life Index ab: Reconstructing magnetic resonance images from undersampled k-space data is a challenging problem. This paper introduces a novel method of image reconstruction from undersampled k-space data based on the concept of singularizing operators and a novel singular k-space model. Exploring the sparsity of an image in the k-space, the singular k-space model (SKM) is proposed in terms of the k-space functions of a singularizing operator. The singularizing operator is constructed by combining basic difference operators. An algorithm is developed to reliably estimate the model parameters from undersampled k-space data. The estimated parameters are then used to recover the missing k-space data through the model, subsequently achieving high-quality reconstruction of the image using inverse Fourier transform. Experiments on physical phantom and real brain MR images have shown that the proposed SKM method constantly outperforms the popular total variation (TV) and the classical zero-filling (ZF) methods regardless of the undersampling rates, the noise levels, and the image structures. For the same objective quality of the reconstructed images, the proposed method requires much less k-space data than the TV method. The SKM method is an effective method for fast MRI reconstruction from the undersampled k-space data. Graphical abstract Two Real Images and their sparsified images by singularizing operator. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|