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Parallel magnetic resonance imaging acceleration with a hybrid sensing approach

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Bibliographic Details
Main Author:	Anh Quang Tran
Other Authors:	Tien-Anh Nguyen
Format:	Article
Language:	English
Published:	Mathematical Biosciences and Engineering 2021
Subjects:	magnetic resonance imaging (MRI) compressed sensing (CS) random under-sampling parallel magnetic resonance imaging (pMRI)
Online Access:	http://www.aimspress.com/article/doi/10.3934/mbe.2021116 https://dlib.phenikaa-uni.edu.vn/handle/PNK/1946
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spelling	oai:localhost:PNK-19462022-08-17T05:54:40Z Parallel magnetic resonance imaging acceleration with a hybrid sensing approach Anh Quang Tran Tien-Anh Nguyen Phuc Thinh Doan Duc-Nghia Tran Duc-Tan Tran magnetic resonance imaging (MRI) compressed sensing (CS) random under-sampling parallel magnetic resonance imaging (pMRI) Q2 In magnetic resonance imaging (MRI), the scan time for acquiring an image is relatively long, resulting in patient uncomfortable and error artifacts. Fortunately, the compressed sensing (CS) and parallel magnetic resonance imaging (pMRI) can reduce the scan time of the MRI without significantly compromising the quality of the images. It has been found that the combination of pMRI and CS can better improve the image reconstruction, which will accelerate the speed of MRI acquisition because the number of measurements is much smaller than that by pMRI. In this paper, we propose combining a combined CS method and pMRI for better accelerating the MRI acquisition. In the combined CS method, the under-sampled data of the K-space is performed by taking both regular sampling and traditional random under-sampling approaches. MRI image reconstruction is then performed by using nonlinear conjugate gradient optimization. The performance of the proposed method is simulated and evaluated using the reconstruction error measure, the universal image quality Q-index, and the peak signal-to-noise ratio (PSNR). The numerical simulations confirmed that, the average error, the Q index, and the PSNR ratio of the appointed scheme are remarkably improved up to 59, 63, and 39% respectively as compared to the traditional scheme. For the first time, instead of using highly computational approaches, a simple and efficient combination of CS and pMRI is proposed for the better MRI reconstruction. These findings are very meaningful for reducing the imaging time of MRI systems. 2021-07-05T09:19:33Z 2021-07-05T09:19:33Z 2021 Article http://www.aimspress.com/article/doi/10.3934/mbe.2021116 https://dlib.phenikaa-uni.edu.vn/handle/PNK/1946 10.3934/mbe.2021116 en application/pdf Mathematical Biosciences and Engineering
institution	Digital Phenikaa
collection	Digital Phenikaa
language	English
topic	magnetic resonance imaging (MRI) compressed sensing (CS) random under-sampling parallel magnetic resonance imaging (pMRI)
spellingShingle	magnetic resonance imaging (MRI) compressed sensing (CS) random under-sampling parallel magnetic resonance imaging (pMRI) Anh Quang Tran Parallel magnetic resonance imaging acceleration with a hybrid sensing approach
description	Q2
author2	Tien-Anh Nguyen
author_facet	Tien-Anh Nguyen Anh Quang Tran
format	Article
author	Anh Quang Tran
author_sort	Anh Quang Tran
title	Parallel magnetic resonance imaging acceleration with a hybrid sensing approach
title_short	Parallel magnetic resonance imaging acceleration with a hybrid sensing approach
title_full	Parallel magnetic resonance imaging acceleration with a hybrid sensing approach
title_fullStr	Parallel magnetic resonance imaging acceleration with a hybrid sensing approach
title_full_unstemmed	Parallel magnetic resonance imaging acceleration with a hybrid sensing approach
title_sort	parallel magnetic resonance imaging acceleration with a hybrid sensing approach
publisher	Mathematical Biosciences and Engineering
publishDate	2021
url	http://www.aimspress.com/article/doi/10.3934/mbe.2021116 https://dlib.phenikaa-uni.edu.vn/handle/PNK/1946
_version_	1751856298012442624
score	8.891053

Parallel magnetic resonance imaging acceleration with a hybrid sensing approach

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