Compressive sensing MRI reconstruction using empirical wavelet transform and grey wolf optimizer

Ragab, Mohamed; Omer, Osama A.; Abdel-Nasser, Mohamed

doi:10.1007/s00521-018-3812-7

Cited by 17 publications

(9 citation statements)

References 46 publications

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“…In (6) determines the velocity update from the original velocity to the current velocity. In (7) is then used to calculate the current position, which is equal to the sum of the new velocity and the old position [31].…”

Section: Pso Theorymentioning

confidence: 99%

“…A CS and deep learning method are employed for quantitative MRI reconstruction [6]. A CS MRI image reconstruction is presented in [7], the method adopts the empirical WT as a sparse feature domain and the grey wolf optimizer, to tune the method parameters. The CS is used with dual-tree complex WT to reconstruct an image, by comparing the distorted image measurements to a reference image [8].…”

Section: Introductionmentioning

confidence: 99%

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PSO based multilevel MRI compression using compressive sensing

Tashan

Kadhim

2022

Bulletin EEI

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A multilevel compression method, for magnetic resonance imaging (MRI) images, is presented in this paper. First, the image is segmented into frames of equal size. Then, the sparsity of each frame is computed. Based on the sparsity index value, each frame is compressive sensing (CS) compressed/reconstructed at one level of four. Particle swarm optimization (PSO) is used to optimize the amount of information to be used in the CS reconstruction process, and to optimize the sparsity thresholds, that separate the different compression levels. Two-dimensional sigmoid function is suggested as a fitness function for the PSO. Six MRI images are used to evaluate the performance of the proposed method. The results show considerable gain in both peak signal to noise ratio (PSNR) and compression level (CL), when compared to single level compression, which is commonly considered in the literature.

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Section: Pso Theorymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

PSO based multilevel MRI compression using compressive sensing

Tashan

Kadhim

2022

Bulletin EEI

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“…The compressive sensing (CS) theory,[ 12 13 14 15 16 17 18 19 ] as a subcategory from SIR methods, has shot to prominence. It is instrumental in reconstructing a reliable and clean image from a limited number of noisy projection measurements.…”

Section: Introductionmentioning

confidence: 99%

“…Although generally speaking an object is not sparse, a sparsifying transform may be used to convert it into a domain in which the signal has a sparse representation. [ 14 17 19 20 ] This method would be able to reconstruct high-quality images from roughly one-tenth of the number of views required by filtered back projection (FBP) in two-dimensional (2D) images, thereby allowing a much lower dose scanning protocol than required in applying conventional reconstruction methods. [ 5 ] However, a major drawback with CS-based reconstruction algorithms is their being prohibitively computationally intensive for clinical use.…”

Section: Introductionmentioning

confidence: 99%

Low-dose cone-beam computed tomography reconstruction through a fast three-dimensional compressed sensing method based on the three-dimensional pseudo-polar fourier transform

2022

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Background: Reconstruction of high quality two dimensional images from fan beam computed tomography (CT) with a limited number of projections is already feasible through Fourier based iterative reconstruction method. However, this article is focused on a more complicated reconstruction of three dimensional (3D) images in a sparse view cone beam computed tomography (CBCT) by utilizing Compressive Sensing (CS) based on 3D pseudo polar Fourier transform (PPFT). Method: In comparison with the prevalent Cartesian grid, PPFT re gridding is potent to remove rebinning and interpolation errors. Furthermore, using PPFT based radon transform as the measurement matrix, reduced the computational complexity. Results: In order to show the computational efficiency of the proposed method, we compare it with an algebraic reconstruction technique and a CS type algorithm. We observed convergence in <20 iterations in our algorithm while others would need at least 50 iterations for reconstructing a qualified phantom image. Furthermore, using a fast composite splitting algorithm solver in each iteration makes it a fast CBCT reconstruction algorithm. The algorithm will minimize a linear combination of three terms corresponding to a least square data fitting, Hessian (HS) Penalty and l1 norm wavelet regularization. We named it PP-based compressed sensing-HS-W. In the reconstruction range of 120 projections around the 360° rotation, the image quality is visually similar to reconstructed images by Feldkamp-Davis-Kress algorithm using 720 projections. This represents a high dose reduction. Conclusion: The main achievements of this work are to reduce the radiation dose without degrading the image quality. Its ability in removing the staircase effect, preserving edges and regions with smooth intensity transition, and producing high-resolution, low-noise reconstruction results in low-dose level are also shown.

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“…Compressed sensing (CS) [1,2] is an emerging framework for data acquisition and reconstruction, which permits us to reconstruct the original sparse or compressible signals from only a small number of linear measurements. CS has been exploited in image processing, such as 3D video [3], medical imaging [4], single-pixel imaging [5]. This is based on the principle that, through optimization, the sparsity of a signal can be recovered from far fewer samples than required by the Nyquist-Shannon sampling theorem when the measurement matrix satisfies the restricted isometry property (RIP) [6].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Weighted High Frequency Iterative Algorithm for Fractional-Order Total Variation with Nonlocal Regularization for Image Reconstruction

Chen¹,

Qin²,

Ren³

et al. 2020

Electronics

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We propose an adaptive weighted high frequency iterative algorithm for a fractional-order total variation (FrTV) approach with nonlocal regularization to alleviate image deterioration and to eliminate staircase artifacts, which result from the total variation (TV) method. The high frequency gradients are reweighted in iterations adaptively when we decompose the image into high and low frequency components using the pre-processing technique. The nonlocal regularization is introduced into our method based on nonlocal means (NLM) filtering, which contains prior image structural information to suppress staircase artifacts. An alternating direction multiplier method (ADMM) is used to solve the problem combining reweighted FrTV and nonlocal regularization. Experimental results show that both the peak signal-to-noise ratios (PSNR) and structural similarity index (SSIM) of reconstructed images are higher than those achieved by the other four methods at various sampling ratios less than 25%. At 5% sampling ratios, the gains of PSNR and SSIM are up to 1.63 dB and 0.0114 from ten images compared with reweighted total variation with nuclear norm regularization (RTV-NNR). The improved approach preserves more texture details and has better visual effects, especially at low sampling ratios, at the cost of taking more time.

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Compressive sensing MRI reconstruction using empirical wavelet transform and grey wolf optimizer

Cited by 17 publications

References 46 publications

PSO based multilevel MRI compression using compressive sensing

PSO based multilevel MRI compression using compressive sensing

Low-dose cone-beam computed tomography reconstruction through a fast three-dimensional compressed sensing method based on the three-dimensional pseudo-polar fourier transform

Adaptive Weighted High Frequency Iterative Algorithm for Fractional-Order Total Variation with Nonlocal Regularization for Image Reconstruction

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