MRI Simulation-based evaluation of an efficient under-sampling approach

Anh Quang Tran; Tien-Anh Nguyen; Van Tu Duong; Quang-Huy Tran; Duc Nghia Tran; Duc-Tan Tran; Anh Quang Tran; Tien-Anh Nguyen; Van Tu Duong; Quang-Huy Tran; Duc Nghia Tran; Duc-Tan Tran

doi:10.3934/mbe.2020224

Mathematical Biosciences and Engineering

2020, Volume 17, Issue 4: 4048-4063. doi: 10.3934/mbe.2020224

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Research article

MRI Simulation-based evaluation of an efficient under-sampling approach

1.
Department of Biomedical Engineering, Le Quy Don Technical University, Ha Noi, Vietnam
2.
Department of Physics, Le Quy Don Technical University, Ha Noi, Vietnam
3.
NTT Hi-Tech Institute, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam
4.
Department of Physics, Hanoi Pedagogical University 2, Vinh Phuc City, Vietnam
5.
Institute of Information Technology, Vietnam Academy of Science and Technology, Vietnam
6.
Department of Electrical and Electronic Engineering, Phenikaa University, Ha Noi, Vietnam
7.
Phenikaa Research and Technology Institute (PRATI), A&A Green Phoenix Group JSC, No.167 Hoang Ngan, Trung Hoa, Cau Giay, Ha Noi, Vietnam

Received: 28 March 2020 Accepted: 29 May 2020 Published: 04 June 2020

Compressive sampling (CS) has been commonly employed in the field of magnetic resonance imaging (MRI) to accurately reconstruct sparse and compressive signals. In a MR image, a large amount of encoded information focuses on the origin of the k-space. For the 2D Cartesian K-space MRI, under-sampling the frequency-encoding (k_x) dimension does not affect to the acquisition time, thus, only the phase-encoding (k_y) dimension can be exploited. In the traditional random under-sampling approach, it acquired Gaussian random measurements along the phaseencoding (k_y) in the k-space. In this paper, we proposed a hybrid under-sampling approach; the number of measurements in (k_y) is divided into two portions: 70% of the measurements are for random under-sampling and 30% are for definite under-sampling near the origin of the k-space. The numerical simulation consequences pointed out that, in the lower region of the under-sampling ratio r, both the average error and the universal image quality index of the appointed scheme are drastically improved up to 55 and 77% respectively as compared to the traditional scheme. For the first time, instead of using highly computational complexity of many advanced reconstruction techniques, a simple and efficient CS method based simulation is proposed for MRI reconstruction improvement. These findings are very useful for designing new MRI data acquisition approaches for reducing the imaging time of current MRI systems.
- MRI,
- compressed sensing,
- power law,
- k-space,
- non-linear conjugate gradient
Citation: Anh Quang Tran, Tien-Anh Nguyen, Van Tu Duong, Quang-Huy Tran, Duc Nghia Tran, Duc-Tan Tran. MRI Simulation-based evaluation of an efficient under-sampling approach[J]. Mathematical Biosciences and Engineering, 2020, 17(4): 4048-4063. doi: 10.3934/mbe.2020224

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Abstract

Compressive sampling (CS) has been commonly employed in the field of magnetic resonance imaging (MRI) to accurately reconstruct sparse and compressive signals. In a MR image, a large amount of encoded information focuses on the origin of the k-space. For the 2D Cartesian K-space MRI, under-sampling the frequency-encoding (k_x) dimension does not affect to the acquisition time, thus, only the phase-encoding (k_y) dimension can be exploited. In the traditional random under-sampling approach, it acquired Gaussian random measurements along the phaseencoding (k_y) in the k-space. In this paper, we proposed a hybrid under-sampling approach; the number of measurements in (k_y) is divided into two portions: 70% of the measurements are for random under-sampling and 30% are for definite under-sampling near the origin of the k-space. The numerical simulation consequences pointed out that, in the lower region of the under-sampling ratio r, both the average error and the universal image quality index of the appointed scheme are drastically improved up to 55 and 77% respectively as compared to the traditional scheme. For the first time, instead of using highly computational complexity of many advanced reconstruction techniques, a simple and efficient CS method based simulation is proposed for MRI reconstruction improvement. These findings are very useful for designing new MRI data acquisition approaches for reducing the imaging time of current MRI systems.

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