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JCR Impact Factor: 0.700
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SCOPUS CiteScore: 1.8
Issues per year: 4
Current issue: Nov 2024
Next issue: Feb 2025
Avg review time: 57 days
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PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

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


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2024-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2023. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.700 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.600.

2023-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

2023-Jun-05
SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

2022-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2021. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.825 (0.722 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.752.

2022-Jun-16
SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2021 is 2.5, the same as for 2020 but better than all our previous results.

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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: 786 | Views: 1,729

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.


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Cited-By CrossRef

[1] McSTRA: A multi-branch cascaded swin transformer for point spread function-guided robust MRI reconstruction, Ekanayake, Mevan, Pawar, Kamlesh, Harandi, Mehrtash, Egan, Gary, Chen, Zhaolin, Computers in Biology and Medicine, ISSN 0010-4825, Issue , 2024.
Digital Object Identifier: 10.1016/j.compbiomed.2023.107775
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Faculty of Electrical Engineering and Computer Science
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