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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2020
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oai:localhost:PNK-5362022-08-17T05:54:37Z 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 MRI compressed sensing power law k-space non-linear conjugate gradient 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 2020-08-13T07:47:19Z 2020-08-13T07:47:19Z 2020 Article https://dlib.phenikaa-uni.edu.vn/handle/PNK/536 10.3934/mbe.2020224 en application/pdf |
| institution |
Digital Phenikaa |
| collection |
Digital Phenikaa |
| language |
English |
| topic |
MRI compressed sensing power law k-space non-linear conjugate gradient |
| spellingShingle |
MRI compressed sensing power law k-space non-linear conjugate gradient 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 |
| description |
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 |
| format |
Article |
| author |
Anh, Quang Tran Tien, Anh Nguyen Van, Tu Duong Quang, Huy Tran Duc, Nghia Tran Duc, Tan Tran |
| author_facet |
Anh, Quang Tran Tien, Anh Nguyen Van, Tu Duong Quang, Huy Tran Duc, Nghia Tran Duc, Tan Tran |
| author_sort |
Anh, Quang Tran |
| title |
MRI Simulation-based evaluation of an efficient under-sampling approach |
| title_short |
MRI Simulation-based evaluation of an efficient under-sampling approach |
| title_full |
MRI Simulation-based evaluation of an efficient under-sampling approach |
| title_fullStr |
MRI Simulation-based evaluation of an efficient under-sampling approach |
| title_full_unstemmed |
MRI Simulation-based evaluation of an efficient under-sampling approach |
| title_sort |
mri simulation-based evaluation of an efficient under-sampling approach |
| publishDate |
2020 |
| url |
https://dlib.phenikaa-uni.edu.vn/handle/PNK/536 |
| _version_ |
1751856276799750144 |
| score |
8.891145 |