Iterative Next‐Neighbor Regridding (INNG): Improved reconstruction from nonuniformly sampled <i>k</i>‐space data using rescaled matrices

Moriguchi, Hisamoto; Duerk, Jeffrey L.

doi:10.1002/mrm.10692

Cited by 23 publications

(24 citation statements)

References 27 publications

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“…Various regridding techniques have been studied in magnetic resonance imaging (MRI) when non-rectilinear data acquisition methods are used. These techniques include conventional regridding algorithms using convolution function [19,20], block uniform resampling (BURS) [21], next-neighbour regridding [22], and the more recent iterative next-neighbour regridding (INNG) [23]. In the present study, we adopted the INNG to regrid the radially sampled spectral data on a rectilinear grid.…”

Section: Regridding Of Radial Spectral Datamentioning

confidence: 99%

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Image Reconstruction from Sparse Projections Using S-Transform

Luo¹,

Liu²,

Li³

et al. 2011

J Math Imaging Vis

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Sparse projections are an effective way to reduce the exposure to radiation during X-ray CT imaging. However, reconstruction of images from sparse projection data is challenging. This paper introduces a new sparse transform, referred to as S-transform, and proposes an accurate image reconstruction method based on the transform. The S-transform effectively converts the ill-posed reconstruction problem into a well-defined one by representing the image using a small set of transform coefficients. An algorithm is proposed that efficiently estimates the S-transform coefficients from the sparse projections, thus allowing the image to be accurately reconstructed using the inverse S-transform. The experimental results on both simulated and real images have consistently shown that, compared to the popular total variation (TV) method, the proposed method achieves comparable results when the projections is sparse, and substantially improves the quality of the reconstructed image when the number of the projections is relatively high. Therefore, the use of the proposed reconstruction algorithm may permit reduction of the radiation exposure without trade-off in imaging performance.

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Section: Regridding Of Radial Spectral Datamentioning

confidence: 99%

“…In the present study, we adopted the INNG to regrid the radially sampled spectral data on a rectilinear grid. For details about INNG, readers are a referred to Moriguchi et al [23].…”

Section: Regridding Of Radial Spectral Datamentioning

confidence: 99%

Image Reconstruction from Sparse Projections Using S-Transform

Luo¹,

Liu²,

Li³

et al. 2011

J Math Imaging Vis

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“…However, the rearrangement of the coordinates is not performed when a projection reconstruction such as the filtered back-projection method is employed for image reconstruction [4,13]. Recently, iterative image reconstruction has been employed in radial MRI [14][15][16]. Iterative image reconstruction has an affinity for the radial scan, because it does not require rearrangement of k-space data.…”

Section: Introductionmentioning

confidence: 98%

Iterative image reconstruction that includes a total variation regularization for radial MRI

Kojima

Shinohara

Hashimoto

et al. 2015

Radiol Phys Technol

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This paper presents an iterative image reconstruction method for radial encodings in MRI based on a total variation (TV) regularization. The algebraic reconstruction method combined with total variation regularization (ART_TV) is implemented with a regularization parameter specifying the weight of the TV term in the optimization process. We used numerical simulations of a Shepp-Logan phantom, as well as experimental imaging of a phantom that included a rectangular-wave chart, to evaluate the performance of ART_TV, and to compare it with that of the Fourier transform (FT) method. The trade-off between spatial resolution and signal-to-noise ratio (SNR) was investigated for different values of the regularization parameter by experiments on a phantom and a commercially available MRI system. ART_TV was inferior to the FT with respect to the evaluation of the modulation transfer function (MTF), especially at high frequencies; however, it outperformed the FT with regard to the SNR. In accordance with the results of SNR measurement, visual impression suggested that the image quality of ART_TV was better than that of the FT for reconstruction of a noisy image of a kiwi fruit. In conclusion, ART_TV provides radial MRI with improved image quality for low-SNR data; however, the regularization parameter in ART_TV is a critical factor for obtaining improvement over the FT.

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“…!,, (x,y) are extracted as f x (x,y) for the next processing.Each iteration means that the resultant frequency spectrum matrix is the convolution of the one before zero replace step with a 20 sinc function to estimate the frequency spectrum. Please refer to[4] for more details.It should be mentioned that the INNG algorithm is self convergent. In step of matrix rescaling, the new location of each datum is determined by rounding off the product of the original k-space coordinate and rescaling factor.…”

mentioning

confidence: 99%

Image reconstruction of sparse fan-beam projections using a hybrid algorithm

Xiao

Luo

2011

Proceedings of 2011 International Conference on Electronics and Optoelectronics

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Image reconstruction from sparse fan-beam projection data would result in image error. In this paper, a hybrid imaging algorithm from sparse fan-beam projections is proposed. The first, high exact sparse spectrum data is extracted from image reconstruction from sparse fan-beam projections by filtered back-projection (FBP), and then image is reconstructed from the data using the iterative next neighbor regridding (INNG) algorithm combined with total variation (TV) gradient descent method. The INNG step can restrict image distortions around the model and the TV gradient descent step can remove small oscillations in the model while preserving edges. The combined method is compared with the original INNG algorithm and TV gradient descent method. Computer simulation results demonstrate that the hybrid algorithm is effective for sparse fan-beam projection reconstruction.

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Iterative Next‐Neighbor Regridding (INNG): Improved reconstruction from nonuniformly sampled k‐space data using rescaled matrices

Cited by 23 publications

References 27 publications

Image Reconstruction from Sparse Projections Using S-Transform

Image Reconstruction from Sparse Projections Using S-Transform

Iterative image reconstruction that includes a total variation regularization for radial MRI

Image reconstruction of sparse fan-beam projections using a hybrid algorithm

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