Highly undersampled MR image reconstruction using an improved dual-dictionary learning method with self-adaptive dictionaries.
Dual-dictionary learning (Dual-DL) method utilizes both a low-resolution dictionary and a high-resolution dictionary, which are co-trained for sparse coding and image updating, respectively. It can effectively exploit a priori knowledge regarding the typical structures, specific features, and local...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 55; no. 5; pp. 807 - 823 |
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| Autores principales: | , , , |
| Formato: | diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
May2017
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| 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=123106930&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 123106930 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: May2017 vid: 55 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 123106930 123106930 143900474 NLM27538399 123106930 10.1007/s11517-016-1556-z NLM27538399 123106930 ppf: 807 ppct: 16 formats: fmt: @attributes: type: P tig: atl: Highly undersampled MR image reconstruction using an improved dual-dictionary learning method with self-adaptive dictionaries. aug: au: Li, Jiansen Song, Ying Zhu, Zhen Zhao, Jun affil: School of Biomedical Engineering , Shanghai Jiao Tong University , 800 Dongchuan Rd., Minhang Shanghai 200240 China sug: subj: Magnetic Resonance Imaging Methods Image Processing, Computer Assisted Methods Learning Brain Physiology Human ab: Dual-dictionary learning (Dual-DL) method utilizes both a low-resolution dictionary and a high-resolution dictionary, which are co-trained for sparse coding and image updating, respectively. It can effectively exploit a priori knowledge regarding the typical structures, specific features, and local details of training sets images. The prior knowledge helps to improve the reconstruction quality greatly. This method has been successfully applied in magnetic resonance (MR) image reconstruction. However, it relies heavily on the training sets, and dictionaries are fixed and nonadaptive. In this research, we improve Dual-DL by using self-adaptive dictionaries. The low- and high-resolution dictionaries are updated correspondingly along with the image updating stage to ensure their self-adaptivity. The updated dictionaries incorporate both the prior information of the training sets and the test image directly. Both dictionaries feature improved adaptability. Experimental results demonstrate that the proposed method can efficiently and significantly improve the quality and robustness of MR image reconstruction. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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