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Segmented Multistage Reconstruction of Magnetic Resonance ImagesFARIS, M. , JAVID, T. , KAZMI, M. , AZIZ, A.
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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)
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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 200] [SCOPUS Times Cited 244]
 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 174] [SCOPUS Times Cited 226]
 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 403] [SCOPUS Times Cited 433]
 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 9] [SCOPUS Times Cited 12]
 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 17026] [SCOPUS Times Cited 21443]
 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 4091] [SCOPUS Times Cited 4520]
 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 579]
 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 6] [SCOPUS Times Cited 8]
 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 42] [SCOPUS Times Cited 44]
 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 99] [SCOPUS Times Cited 110]
 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 6] [SCOPUS Times Cited 7]
 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.
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