Regularization in parallel MR image reconstruction

Omer, Hammad; Dickinson, Robert

doi:10.1002/cmr.a.20206

Cited by 14 publications

(9 citation statements)

References 15 publications

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“…In practice, voxel exclusion in SENSE unfolding is achieved through regularization instead of masking, as is clearly explained by Omer and Dickinson . There are no obvious reasons why this would not be compatible with rFOX inclusion in the coil sensitivity maps.…”

Section: Discussionmentioning

confidence: 99%

“…This is an additional effort to be performed before starting a new study that focusses on a particular region of interest, but results in a more effective scan protocol of which the cumulative benefits will be paramount when applied to a large number of examinations. In practice, voxel exclusion in SENSE unfolding is achieved through regularization instead of masking, as is clearly explained by Omer and Dickinson (28). There are no obvious reasons why this would not be compatible with rFOX inclusion in the coil sensitivity maps.…”

Section: Discussionmentioning

confidence: 99%

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Combining a reduced field of excitation with SENSE‐based parallel imaging for maximum imaging efficiency

Mooiweer

Sbrizzi

Raaijmakers

et al. 2016

Magnetic Resonance in Med

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PurposeTo show that a combination of parallel imaging using sensitivity encoding (SENSE) and inner volume imaging (IVI) combines the known benefits of both techniques. SENSE with a reduced field of excitation (rFOX) is termed rSENSE.Theory and MethodsThe noise level in SENSE reconstructions is reduced by removing voxels from the unfolding process that are rendered silent by using rFOX. The silent voxels need to be identified beforehand, this is done by using rFOX in the coil sensitivity maps. In vivo experiments were performed at 7 Tesla using a 32‐channel receive coil.ResultsGood image quality could be obtained in vivo with rSENSE at acceleration factors that are higher than could be obtained using SENSE or IVI alone. With rSENSE we were also able to accelerate scans using an rFOX that was purposely designed to be imperfect or incompatible at all with IVI.ConclusionrSENSE has been demonstrated in vivo with two‐dimensionally selective radiofrequency pulses. Besides allowing additional scan acceleration, it offers a greater robustness and flexibility than IVI. The proposed method can be used with other field strengths, anatomies and other rFOX techniques. Magn Reson Med 78:88–96, 2017. © 2016 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution Non Commercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Combining a reduced field of excitation with SENSE‐based parallel imaging for maximum imaging efficiency

Mooiweer

Sbrizzi

Raaijmakers

et al. 2016

Magnetic Resonance in Med

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“…The other way to decrease the MRI scanning time is to under-sample the kspace by leaving out some of the encoding lines. This results in a smaller field of view (FOV) which significantly reduces signal-to-noise ratio (SNR) [18].…”

Section: Cs-mri and Pocs Recoverymentioning

confidence: 99%

Compressively Sampled MR Image Reconstruction Using Hyperbolic Tangent-Based Soft-Thresholding

et al. 2015

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The application of compressed sensing (CS) to magnetic resonance (MR) images utilizes the transformed domain sparsity to enable the reconstruction from an under-sampled k-space (Fourier) data using a non-linear recovery algorithm. In order to estimate the missing k-space data from the partial Fourier samples, the reconstruction algorithms minimize an objective function based on mixed l 1 -l 2 norms. Iterative-shrinkage algorithms, such as parallel coordinate descent (PCD) and separable surrogate functional, provide an efficient numerical technique to minimize the l 1 -regularized least square optimization problem. These algorithms require a thresholding step to induce sparsity in the solution, which is an essential requirement of the CS recovery. This paper introduces a novel soft-thresholding method based on the hyperbolic tangent function. It has been shown that by using the proposed thresholding function in the sparsifying domain and a data consistency step in the k-space, the iterative-shrinkage algorithms can be used effectively to recover the under-sampled MR images. For the purpose of demonstration, we use the proposed soft-thresholding and data consistency with the PCD algorithm and compare its performance with the conventional PCD, projection onto convex sets and low-resolution reconstruction methods. The metrics used to compare the various algorithms are the artifact power, the peak signal-to-noise ratio, the correlation and the structural similarity index. The experimental results are validated using SheppLogan phantom image as well as real human head MR images taken from the MRI scanner at

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“…ISNR is expected to be positive with a better image quality for higher values. Artifact power (AP) is another evaluation parameter of reconstruction that has been derived from “square difference error,” which can be computed as (Omer and Dickinson, ):

AP = \frac{\sum_{i} {| x true(i true) - \hat{bold-italicx} (i) |}^{2}}{\sum_{i, j} {| x (i) |}^{2}}

…”

Section: The Proposed Algorithmsmentioning

confidence: 99%

A modified POCS‐based reconstruction method for compressively sampled MR imaging

Shah

Qureshi

Omer

et al. 2014

Int J Imaging Syst Tech

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One of the challenging tasks in the application of compressed sensing to magnetic resonance imaging is the reconstruction algorithm that can faithfully recover the MR image from randomly undersampled k‐space data. The nonlinear recovery algorithms based on iterative shrinkage start with a single initial guess and use soft‐thresholding to recover the original MR image from the partial Fourier data. This article presents a novel method based on projection onto convex set (POCS) algorithm but it takes two images and then randomly combines them at each iteration to estimate the original MR image. The performance of the proposed method is validated using the original data taken from the MRI scanner at St. Mary's Hospital, London. The experimental results show that the proposed method can reconstruct the original MR image from variable density undersampling scheme in less number of iterations and exhibits better performance in terms of improved signal‐to‐noise ratio, artifact power, and correlation as compared to the reconstruction through low‐resolution and POCS algorithms. © 2014 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 24, 203–207, 2014

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Regularization in parallel MR image reconstruction

Cited by 14 publications

References 15 publications

Combining a reduced field of excitation with SENSE‐based parallel imaging for maximum imaging efficiency

Combining a reduced field of excitation with SENSE‐based parallel imaging for maximum imaging efficiency

Compressively Sampled MR Image Reconstruction Using Hyperbolic Tangent-Based Soft-Thresholding

A modified POCS‐based reconstruction method for compressively sampled MR imaging

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