Robust GRAPPA reconstruction and its evaluation with the perceptual difference model.
Purpose: To develop and optimize a new modification of GRAPPA (generalized autocalibrating partially parallel acquisitions) MR reconstruction algorithm named "Robust GRAPPA."Materials and Methods: In Robust GRAPPA, k-space data points were weighted before the reconstruction. Small or zero weights we...
| Publicado en: | Journal of Magnetic Resonance Imaging Vol. 27; no. 6; pp. 1412 - 1421 |
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| Autores principales: | , , , |
| Formato: | research Journal Article |
| Publicado: |
Wiley-Blackwell
Jun2008
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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=105659573&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105659573 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10531807 O63 jtl: Journal of Magnetic Resonance Imaging issn: 10531807 maglogo: Y pubinfo: dt: Jun2008 vid: 27 iid: 6 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 105659573 105659573 NLM18504764 2010010797 10.1002/jmri.21352 NLM18504764 PMC4484794 105659573 ppf: 1412 ppct: 9 formats: tig: atl: Robust GRAPPA reconstruction and its evaluation with the perceptual difference model. aug: au: Huo D Wilson DL Huo, Donglai Wilson, David L affil: Keller Center for Imaging Innovation, Barrow Neurological Institute, Phoenix, Arizona, USA sug: subj: Algorithms Image Processing, Computer Assisted Methods Magnetic Resonance Imaging Methods Models, Theoretical Image Enhancement Methods Observer Bias Time Factors Human ab: Purpose: To develop and optimize a new modification of GRAPPA (generalized autocalibrating partially parallel acquisitions) MR reconstruction algorithm named "Robust GRAPPA."Materials and Methods: In Robust GRAPPA, k-space data points were weighted before the reconstruction. Small or zero weights were assigned to "outliers" in k-space. We implemented a Slow Robust GRAPPA method, which iteratively reweighted the k-space data. It was compared to an ad hoc Fast Robust GRAPPA method, which eliminated (assigned zero weights to) a fixed percentage of k-space "outliers" following an initial estimation procedure. In comprehensive experiments the new algorithms were evaluated using the perceptual difference model (PDM), whereby image quality was quantitatively compared to the reference image. Independent variables included algorithm type, total reduction factor, outlier ratio, center filling options, and noise across multiple image datasets, providing 10,800 test images for evaluation.Results: The Fast Robust GRAPPA method gave results very similar to Slow Robust GRAPPA, and showed significant improvements as compared to regular GRAPPA. Fast Robust GRAPPA added little computation time compared with regular GRAPPA.Conclusion: Robust GRAPPA was proposed and proved useful for improving the reconstructed image quality. PDM was helpful in designing and optimizing the MR reconstruction algorithms. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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