Low‐rank and sparse reconstruction for fast diffusion nuclear magnetic resonance spectroscopy
Abstract Nuclear magnetic resonance with diffusion‐ordered spectroscopy (DOSY) serves as an important analytical tool to non‐destructively separate a molecule from a compound in medicine and chemistry. However, the data acquisition time increases rapidly for multidimensional DOSY. To enable fast DOS...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Hindawi-IET
2021-04-01
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| Series: | IET Signal Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/sil2.12022 |
| _version_ | 1797421777061675008 |
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| author | Di Guo Jiaying Zhan Yirong Zhou Zhangren Tu Zifei Zhang Zhong Chen Xiaobo Qu |
| author_facet | Di Guo Jiaying Zhan Yirong Zhou Zhangren Tu Zifei Zhang Zhong Chen Xiaobo Qu |
| author_sort | Di Guo |
| collection | DOAJ |
| description | Abstract Nuclear magnetic resonance with diffusion‐ordered spectroscopy (DOSY) serves as an important analytical tool to non‐destructively separate a molecule from a compound in medicine and chemistry. However, the data acquisition time increases rapidly for multidimensional DOSY. To enable fast DOSY, partial data are acquired with non‐uniform sampling, and the spectrum can be reconstructed with a proper constraint, such as sparsity in the state‐of‐the‐art method. However, the reconstructed spectrum is observed to have isolated artefacts, which can be easily recognised as fake peaks and affect the estimated diffusion coefficients severely. The authors introduce the low‐rank constraint as an effective remedy to remove these artefacts and derive a fast algorithm to solve the reconstruction problem. Results on both synthetic and realistic DOSY spectra show that a better spectrum and more accurate diffusion coefficients can be achieved. |
| first_indexed | 2024-03-09T07:22:17Z |
| format | Article |
| id | doaj.art-ce7434faf3a94281aee7acca546fff1a |
| institution | Directory Open Access Journal |
| issn | 1751-9675 1751-9683 |
| language | English |
| last_indexed | 2024-03-09T07:22:17Z |
| publishDate | 2021-04-01 |
| publisher | Hindawi-IET |
| record_format | Article |
| series | IET Signal Processing |
| spelling | doaj.art-ce7434faf3a94281aee7acca546fff1a2023-12-03T07:19:47ZengHindawi-IETIET Signal Processing1751-96751751-96832021-04-01152889710.1049/sil2.12022Low‐rank and sparse reconstruction for fast diffusion nuclear magnetic resonance spectroscopyDi Guo0Jiaying Zhan1Yirong Zhou2Zhangren Tu3Zifei Zhang4Zhong Chen5Xiaobo Qu6School of Computer and Information Engineering Xiamen University of Technology Xiamen ChinaSchool of Computer and Information Engineering Xiamen University of Technology Xiamen ChinaDepartment of Electronic Science Xiamen University Xiamen ChinaSchool of Computer and Information Engineering Xiamen University of Technology Xiamen ChinaDepartment of Electronic Science Xiamen University Xiamen ChinaDepartment of Electronic Science Xiamen University Xiamen ChinaDepartment of Electronic Science Xiamen University Xiamen ChinaAbstract Nuclear magnetic resonance with diffusion‐ordered spectroscopy (DOSY) serves as an important analytical tool to non‐destructively separate a molecule from a compound in medicine and chemistry. However, the data acquisition time increases rapidly for multidimensional DOSY. To enable fast DOSY, partial data are acquired with non‐uniform sampling, and the spectrum can be reconstructed with a proper constraint, such as sparsity in the state‐of‐the‐art method. However, the reconstructed spectrum is observed to have isolated artefacts, which can be easily recognised as fake peaks and affect the estimated diffusion coefficients severely. The authors introduce the low‐rank constraint as an effective remedy to remove these artefacts and derive a fast algorithm to solve the reconstruction problem. Results on both synthetic and realistic DOSY spectra show that a better spectrum and more accurate diffusion coefficients can be achieved.https://doi.org/10.1049/sil2.12022computerised instrumentationdata acquisitiondiffusionNMR spectroscopynuclear magnetic resonance |
| spellingShingle | Di Guo Jiaying Zhan Yirong Zhou Zhangren Tu Zifei Zhang Zhong Chen Xiaobo Qu Low‐rank and sparse reconstruction for fast diffusion nuclear magnetic resonance spectroscopy IET Signal Processing computerised instrumentation data acquisition diffusion NMR spectroscopy nuclear magnetic resonance |
| title | Low‐rank and sparse reconstruction for fast diffusion nuclear magnetic resonance spectroscopy |
| title_full | Low‐rank and sparse reconstruction for fast diffusion nuclear magnetic resonance spectroscopy |
| title_fullStr | Low‐rank and sparse reconstruction for fast diffusion nuclear magnetic resonance spectroscopy |
| title_full_unstemmed | Low‐rank and sparse reconstruction for fast diffusion nuclear magnetic resonance spectroscopy |
| title_short | Low‐rank and sparse reconstruction for fast diffusion nuclear magnetic resonance spectroscopy |
| title_sort | low rank and sparse reconstruction for fast diffusion nuclear magnetic resonance spectroscopy |
| topic | computerised instrumentation data acquisition diffusion NMR spectroscopy nuclear magnetic resonance |
| url | https://doi.org/10.1049/sil2.12022 |
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