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Article: M4Raw: A multi-contrast, multi-repetition, multi-channel MRI k-space dataset for low-field MRI research

TitleM4Raw: A multi-contrast, multi-repetition, multi-channel MRI k-space dataset for low-field MRI research
Authors
Issue Date10-May-2023
PublisherNature Research
Citation
Scientific Data, 2023, v. 10, n. 1 How to Cite?
AbstractRecently, low-field magnetic resonance imaging (MRI) has gained renewed interest to promote MRI accessibility and affordability worldwide. The presented M4Raw dataset aims to facilitate methodology development and reproducible research in this field. The dataset comprises multi-channel brain k-space data collected from 183 healthy volunteers using a 0.3 Tesla whole-body MRI system, and includes T1-weighted, T2-weighted, and fluid attenuated inversion recovery (FLAIR) images with in-plane resolution of similar to 1.2 mm and through-plane resolution of 5 mm. Importantly, each contrast contains multiple repetitions, which can be used individually or to form multi-repetition averaged images. After excluding motion-corrupted data, the partitioned training and validation subsets contain 1024 and 240 volumes, respectively. To demonstrate the potential utility of this dataset, we trained deep learning models for image denoising and parallel imaging tasks and compared their performance with traditional reconstruction methods. This M4Raw dataset will be valuable for the development of advanced data-driven methods specifically for low-field MRI. It can also serve as a benchmark dataset for general MRI reconstruction algorithms.
Persistent Identifierhttp://hdl.handle.net/10722/337257
ISSN
2023 Impact Factor: 5.8
2023 SCImago Journal Rankings: 1.937
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLyu, M-
dc.contributor.authorMei, LF-
dc.contributor.authorHuang, SJ-
dc.contributor.authorLiu, SX-
dc.contributor.authorLi, Y-
dc.contributor.authorYang, KX-
dc.contributor.authorLiu, YL-
dc.contributor.authorDong, Y-
dc.contributor.authorDong, LZ-
dc.contributor.authorWu, EX-
dc.date.accessioned2024-03-11T10:19:18Z-
dc.date.available2024-03-11T10:19:18Z-
dc.date.issued2023-05-10-
dc.identifier.citationScientific Data, 2023, v. 10, n. 1-
dc.identifier.issn2052-4463-
dc.identifier.urihttp://hdl.handle.net/10722/337257-
dc.description.abstractRecently, low-field magnetic resonance imaging (MRI) has gained renewed interest to promote MRI accessibility and affordability worldwide. The presented M4Raw dataset aims to facilitate methodology development and reproducible research in this field. The dataset comprises multi-channel brain k-space data collected from 183 healthy volunteers using a 0.3 Tesla whole-body MRI system, and includes T1-weighted, T2-weighted, and fluid attenuated inversion recovery (FLAIR) images with in-plane resolution of similar to 1.2 mm and through-plane resolution of 5 mm. Importantly, each contrast contains multiple repetitions, which can be used individually or to form multi-repetition averaged images. After excluding motion-corrupted data, the partitioned training and validation subsets contain 1024 and 240 volumes, respectively. To demonstrate the potential utility of this dataset, we trained deep learning models for image denoising and parallel imaging tasks and compared their performance with traditional reconstruction methods. This M4Raw dataset will be valuable for the development of advanced data-driven methods specifically for low-field MRI. It can also serve as a benchmark dataset for general MRI reconstruction algorithms.-
dc.languageeng-
dc.publisherNature Research-
dc.relation.ispartofScientific Data-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titleM4Raw: A multi-contrast, multi-repetition, multi-channel MRI k-space dataset for low-field MRI research-
dc.typeArticle-
dc.identifier.doi10.1038/s41597-023-02181-4-
dc.identifier.pmid37164976-
dc.identifier.scopuseid_2-s2.0-85158873835-
dc.identifier.volume10-
dc.identifier.issue1-
dc.identifier.eissn2052-4463-
dc.identifier.isiWOS:000985826000001-
dc.publisher.placeBERLIN-
dc.identifier.issnl2052-4463-

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