|4/2021 - 12|
Segmented Multistage Reconstruction of Magnetic Resonance ImagesFARIS, M. , JAVID, T. , KAZMI, M. , AZIZ, A.
|View the paper record and citations in|
|Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science|
|Download PDF (2,782 KB) | Citation | Downloads: 428 | Views: 396|
compressed sensing, Fourier transforms, image reconstruction, magnetic resonance imaging, spatial resolution
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
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|
| J. P. De Wilde, A. W. Rivers, D. L. Price, "A review of the current use of magnetic resonance imaging in pregnancy and safety implications for the fetus," Progress in Biophysics and Molecular Biology, vol. 87, no. 2-3, pp. 335-353, 2005. |
[CrossRef] [Web of Science Times Cited 201] [SCOPUS Times Cited 249]
 B. M. Dale, M. A. Brown, R. C. Semelka. MRI: Basic Principles and Applications. John Wiley & Sons, pp. 1-3, 2015.
 R. Chartrand, "Fast algorithms for nonconvex compressive sensing: MRI reconstruction from very few data," in International Symposium on Biomedical Imaging: From Nano to Macro, Boston, 2009, pp. 262-265.
[CrossRef] [Web of Science Times Cited 177] [SCOPUS Times Cited 229]
 U. Gamper, P. Boesiger, S. Kozerke, "Compressed sensing in dynamic MRI," Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, vol. 59, no. 2, pp. 365-373, 2008.
[CrossRef] [Web of Science Times Cited 409] [SCOPUS Times Cited 439]
 N. Zhao, D. O'Connor, A. Basarab, D. Ruan, K. Sheng, "Motion compensated dynamic MRI reconstruction with local affine optical flow estimation," IEEE Transactions on Biomedical Engineering, vol. 66, no. 11, pp. 3050-3059, 2019.
[CrossRef] [Web of Science Times Cited 10] [SCOPUS Times Cited 13]
 B. Chen, K. Zhao, B. Li, W. Cai, X. Wang, J. Zhang, J. Fang, "High temporal resolution dynamic contrast-enhanced MRI using compressed sensing-combined sequence in quantitative renal perfusion measurement," Magnetic Resonance Imaging, vol. 33, no. 8, pp. 962-969, 2015.
[CrossRef] [Web of Science Times Cited 4] [SCOPUS Times Cited 4]
 D. L. Donoho, "Compressed sensing," IEEE Transactions on Information Theory, vol. 52, no. 4, pp. 1289-1306, 2006.
[CrossRef] [Web of Science Times Cited 17319] [SCOPUS Times Cited 21815]
 M. Lustig, D. Donoho, J. M. Pauly, "Sparse MRI: The application of compressed sensing for rapid MR imaging," Magnetic Resonance in Medicine, vol. 58, no. 6, pp. 1182-1195, 2007.
[CrossRef] [Web of Science Times Cited 4197] [SCOPUS Times Cited 4614]
 I. Guyon, A. Elisseeff, "An introduction to variable and feature selection," Journal of Machine Learning Research, vol. 3 (Mar), pp. 1157-1182, 2003.
 A. Cohen, W. Dahmen, R. DeVore, "Compressed sensing and best k-term approximation," Journal of the American Mathematical Society, vol.22, no. 1, pp.211-231, 2009.
[CrossRef] [SCOPUS Times Cited 587]
 A. S. Konar, N. N. Vajuvalli, R. Rao, D. Jain, D. R. Babu, S. Geethanath, "Accelerated dynamic contrast enhanced MRI based on region of interest compressed sensing," Magnetic Resonance Imaging, vol. 67, pp. 18-23, 2020.
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 9]
 H. H. Schild. MRI Made Easy. Berlex Laboratories, pp. 53-56, 1994
 J. T. Bushberg, J. A. Seibert, E. M. Leidholdt Jr, J. M. Boone. The Essential Physics of Medical Imaging. Lippincott Williams & Wilkins, pp. 446-448, 2012
 M. Hong, Y. Yu, H. Wang, F. Liu, S. Crozier, "Compressed sensing MRI with singular value decomposition-based sparsity basis," Physics in Medicine & Biology, vol. 56, no. 19, 6311-6325, 2011.
[CrossRef] [Web of Science Times Cited 43] [SCOPUS Times Cited 45]
 J. Ma, "Improved iterative curvelet thresholding for compressed sensing and measurement," IEEE Transactions on Instrumentation and Measurement, vol. 60, no. 1, pp. 126-136, 2011.
[CrossRef] [Web of Science Times Cited 46] [SCOPUS Times Cited 59]
 Y. G. Cen, X. F. Chen, L. H. Cen LH, S. M. Chen, "Compressed sensing based on the single layer wavelet transform for image processing," Journal on Communications, vol. 31, no. 8A, pp. 52-55, 2010.
 Z. Lai, X. Qu, Y. Liu, D. Guo, J. Ye, Z. Zhan, Z. Chen, "Image reconstruction of compressed sensing MRI using graph-based redundant wavelet transform," Medical Image Analysis, vol. 27, pp. 93-104, 2016.
[CrossRef] [Web of Science Times Cited 105] [SCOPUS Times Cited 115]
 R. C. Gonzalez, R. E. Woods, S. L. Eddins. Digital Image Processing Using MATLAB. Gatesmark, pp. 479-484, 2020
 A. Majumdar, R. K. Ward, "An algorithm for sparse MRI reconstruction by Schatten p-norm minimization," Magnetic Resonance Imaging, vol. 29, no. 3, pp. 408-417, 2011.
[CrossRef] [Web of Science Times Cited 76] [SCOPUS Times Cited 80]
 Y. Yang, F. Liu, W. Xu, S. Crozier, "Compressed sensing MRI via two-stage reconstruction," IEEE Transactions on Biomedical Engineering, vol. 62, no. 1, pp. 110-118, 2015,
[CrossRef] [Web of Science Times Cited 20] [SCOPUS Times Cited 25]
 L. Sun, Z. Fan, X. Ding, C. Cai, Y. Huang, J. Paisley, "A divide-and-conquer approach to compressed sensing MRI," Magnetic Resonance Imaging, vol. 63, pp. 37-48, 2019.
[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 2]
 S. Ma, H. Du, W. Mei, "A two-step low rank matrices approach for constrained MR image reconstruction," Magnetic Resonance Imaging, vol. 60, pp. 20-31, 2019.
[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 3]
 T. Nguyen-Duc, T. M. Quan, W. K. Jeong, "Frequency-splitting dynamic MRI reconstruction using multi-scale 3D convolutional sparse coding and automatic parameter selection," Medical Image Analysis, vol. 53, pp. 179-196, 2019.
[CrossRef] [Web of Science Times Cited 13] [SCOPUS Times Cited 14]
 Y. Zhu, W. Shen, F. Cheng, C. Jin, G. Cao, "Removal of high density Gaussian noise in compressed sensing MRI reconstruction through modified total variation image denoising method," Heliyon, vol. 6, no. 3, e03680, 2020.
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 8]
 M. Faris, T. Javid, S. H. Rizvi, A. Aziz, "Segmented region based reconstruction of magnetic resonance image," in International Conference on Computer and Information Sciences, Kuching, 2021, pp. 68-73.
[CrossRef] [SCOPUS Times Cited 1]
 J. Cheng, "Brain Tumor Dataset," 2017. Accessed on 18 April 2021.
 U. Sara, M. Akter, M. S. Uddin, "Image quality assessment through FSIM, SSIM, MSE and PSNRâa comparative study," Journal of Computer and Communications, vol. 7, no. 3, pp. 8-18, 2019.
Web of Science® Citations for all references: 22,638 TCR
SCOPUS® Citations for all references: 28,311 TCR
Web of Science® Average Citations per reference: 809 ACR
SCOPUS® Average Citations per reference: 1,011 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 2022-09-29 02:36 in 148 seconds.
Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.