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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
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ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  4/2021 - 12

Segmented Multistage Reconstruction of Magnetic Resonance Images

FARIS, M. See more information about FARIS, M. on SCOPUS See more information about FARIS, M. on IEEExplore See more information about FARIS, M. on Web of Science, JAVID, T. See more information about  JAVID, T. on SCOPUS See more information about  JAVID, T. on SCOPUS See more information about JAVID, T. on Web of Science, KAZMI, M. See more information about  KAZMI, M. on SCOPUS See more information about  KAZMI, M. on SCOPUS See more information about KAZMI, M. on Web of Science, AZIZ, A. See more information about AZIZ, A. on SCOPUS See more information about AZIZ, A. on SCOPUS See more information about AZIZ, A. on Web of Science
 
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Download PDF pdficon (2,782 KB) | Citation | Downloads: 372 | Views: 312

Author keywords
compressed sensing, Fourier transforms, image reconstruction, magnetic resonance imaging, spatial resolution

References keywords
sensing(14), resonance(10), magnetic(10), reconstruction(9), imaging(9), image(9), jmri(5), dynamic(5), medicine(4), chen(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2021-11-30
Volume 21, Issue 4, Year 2021, On page(s): 107 - 114
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2021.04012
Web of Science Accession Number: 000725107100012
SCOPUS ID: 85122239175

Abstract
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Compressed sensing of magnetic resonance imaging refers to the reconstruction of magnetic resonance images from partially sampled k-space data. The k-space data reduces reconstruction processing time but at the cost of increasing artifacts - especially with the higher reduction factor of the raw data. This work proposes a segmented region-based reconstruction technique to reduce image artifacts with enhanced quality and high temporal resolution. The proposed method segments partially sampled k-space data in two segments according to their frequencies. Lower frequency components at the central region are selected and predicted using nuclear norm minimization. This part and the peripheral part of the k-space components at higher frequencies are merged. The recovery technique iterates to reconstruct more accurate images in terms of conventional compressed sensing techniques. The performance of the proposed method is evaluated and compared with compressed sensing, two-stage compressed sensing, and modified total variation technique. Better results in term of normalized mean square error NMSE, reconstruction time and structural similarity index measure SSIM show the effectiveness of the proposed method with a high reduction factor of data.


References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 22,219 TCR
SCOPUS® Citations for all references: 27,814 TCR

Web of Science® Average Citations per reference: 794 ACR
SCOPUS® Average Citations per reference: 993 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2022-06-30 03:34 in 144 seconds.




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