Self-Learned Kernel Low Rank Approach TO Accelerated High Resolution 3D Diffusion MRI
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2021
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| _version_ | 1866929559311482880 |
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| author | Baul, Abhijit Wang, Nian Zhang, Choyi Ying, Leslie Chang, Yuchou Nakarmi, Ukash |
| author_facet | Baul, Abhijit Wang, Nian Zhang, Choyi Ying, Leslie Chang, Yuchou Nakarmi, Ukash |
| contents | Diffusion Magnetic Resonance Imaging (dMRI) is a promising method to analyze the subtle changes in the tissue structure. However, the lengthy acquisition time is a major limitation in the clinical application of dMRI. Different image acquisition techniques such as parallel imaging, compressed sensing, has shortened the prolonged acquisition time but creating high-resolution 3D dMRI slices still requires a significant amount of time. In this study, we have shown that high-resolution 3D dMRI can be reconstructed from the highly undersampled k-space and q-space data using a Kernel LowRank method. Our proposed method has outperformed the conventional CS methods in terms of both image quality and diffusion maps constructed from the diffusion-weighted images |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2110_08622 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Self-Learned Kernel Low Rank Approach TO Accelerated High Resolution 3D Diffusion MRI Baul, Abhijit Wang, Nian Zhang, Choyi Ying, Leslie Chang, Yuchou Nakarmi, Ukash Image and Video Processing Diffusion Magnetic Resonance Imaging (dMRI) is a promising method to analyze the subtle changes in the tissue structure. However, the lengthy acquisition time is a major limitation in the clinical application of dMRI. Different image acquisition techniques such as parallel imaging, compressed sensing, has shortened the prolonged acquisition time but creating high-resolution 3D dMRI slices still requires a significant amount of time. In this study, we have shown that high-resolution 3D dMRI can be reconstructed from the highly undersampled k-space and q-space data using a Kernel LowRank method. Our proposed method has outperformed the conventional CS methods in terms of both image quality and diffusion maps constructed from the diffusion-weighted images |
| title | Self-Learned Kernel Low Rank Approach TO Accelerated High Resolution 3D Diffusion MRI |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2110.08622 |