MRI Simulation-based evaluation of an efficient under-sampling approach
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-sampli...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://dlib.phenikaa-uni.edu.vn/handle/PNK/536 |
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| Summary: | 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 (kx) dimension does not affect to the
acquisition time, thus, only the phase-encoding (ky) dimension can be exploited. In the traditional
random under-sampling approach, it acquired Gaussian random measurements along the phaseencoding (ky) in the k-space. In this paper, we proposed a hybrid under-sampling approach; the
number of measurements in (ky) 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 |
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