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; Anh Quang Tran; Tien-Anh Nguyen; Phuc Thinh Doan; Duc-Nghia Tran; Duc-Tan Tran

doi:10.3934/mbe.2021116

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 3: 2288-2302. doi: 10.3934/mbe.2021116

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Parallel magnetic resonance imaging acceleration with a hybrid sensing approach

1.
Department of Biomedical Engineering, Le Quy Don Technical University, Hanoi City, Vietnam
2.
Department of Physics, Le Quy Don Technical University, Hanoi City, Vietnam
3.
NTT Hi-Tech Institute, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam
4.
Faculty of Mechanical, Electrical, Electronic and Automotive Engineering, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam
5.
Institute of Information Technology, Vietnam Academy of Science and Technology, Hanoi City, Vietnam
6.
Faculty of Electrical and Electronic Engineering, Phenikaa University, Hanoi 12116, Vietnam

Received: 17 January 2021 Accepted: 02 March 2021 Published: 08 March 2021

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.
- magnetic resonance imaging (MRI),
- compressed sensing (CS),
- random under-sampling,
- parallel magnetic resonance imaging (pMRI),
- K-space
Citation: Anh Quang Tran, Tien-Anh Nguyen, Phuc Thinh Doan, Duc-Nghia Tran, Duc-Tan Tran. Parallel magnetic resonance imaging acceleration with a hybrid sensing approach[J]. Mathematical Biosciences and Engineering, 2021, 18(3): 2288-2302. doi: 10.3934/mbe.2021116

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Abstract

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.

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