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- Publisher Website: 10.1126/science.adm7168
- Scopus: eid_2-s2.0-85192845493
- PMID: 38723062
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Article: Whole-body magnetic resonance imaging at 0.05 Tesla
Title | Whole-body magnetic resonance imaging at 0.05 Tesla |
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Authors | |
Issue Date | 10-May-2024 |
Publisher | American Association for the Advancement of Science |
Citation | Science, 2024, v. 384, n. 6696, p. eadm7168 How to Cite? |
Abstract | Despite a half-century of advancements, global magnetic resonance imaging (MRI) accessibility remains limited and uneven, hindering its full potential in health care. Initially, MRI development focused on low fields around 0.05 Tesla, but progress halted after the introduction of the 1.5 Tesla whole-body superconducting scanner in 1983. Using a permanent 0.05 Tesla magnet and deep learning for electromagnetic interference elimination, we developed a whole-body scanner that operates using a standard wall power outlet and without radiofrequency and magnetic shielding. We demonstrated its wide-ranging applicability for imaging various anatomical structures. Furthermore, we developed three-dimensional deep learning reconstruction to boost image quality by harnessing extensive high-field MRI data. These advances pave the way for affordable deep learning-powered ultra-low-field MRI scanners, addressing unmet clinical needs in diverse health care settings worldwide. |
Persistent Identifier | http://hdl.handle.net/10722/348665 |
ISSN | 2023 Impact Factor: 44.7 2023 SCImago Journal Rankings: 11.902 |
DC Field | Value | Language |
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dc.contributor.author | Zhao, Yujiao | - |
dc.contributor.author | Ding, Ye | - |
dc.contributor.author | Lau, Vick | - |
dc.contributor.author | Man, Christopher | - |
dc.contributor.author | Su, Shi | - |
dc.contributor.author | Xiao, Linfang | - |
dc.contributor.author | Leong, Alex TL | - |
dc.contributor.author | Wu, Ed X | - |
dc.date.accessioned | 2024-10-11T00:31:20Z | - |
dc.date.available | 2024-10-11T00:31:20Z | - |
dc.date.issued | 2024-05-10 | - |
dc.identifier.citation | Science, 2024, v. 384, n. 6696, p. eadm7168 | - |
dc.identifier.issn | 0036-8075 | - |
dc.identifier.uri | http://hdl.handle.net/10722/348665 | - |
dc.description.abstract | Despite a half-century of advancements, global magnetic resonance imaging (MRI) accessibility remains limited and uneven, hindering its full potential in health care. Initially, MRI development focused on low fields around 0.05 Tesla, but progress halted after the introduction of the 1.5 Tesla whole-body superconducting scanner in 1983. Using a permanent 0.05 Tesla magnet and deep learning for electromagnetic interference elimination, we developed a whole-body scanner that operates using a standard wall power outlet and without radiofrequency and magnetic shielding. We demonstrated its wide-ranging applicability for imaging various anatomical structures. Furthermore, we developed three-dimensional deep learning reconstruction to boost image quality by harnessing extensive high-field MRI data. These advances pave the way for affordable deep learning-powered ultra-low-field MRI scanners, addressing unmet clinical needs in diverse health care settings worldwide. | - |
dc.language | eng | - |
dc.publisher | American Association for the Advancement of Science | - |
dc.relation.ispartof | Science | - |
dc.title | Whole-body magnetic resonance imaging at 0.05 Tesla | - |
dc.type | Article | - |
dc.identifier.doi | 10.1126/science.adm7168 | - |
dc.identifier.pmid | 38723062 | - |
dc.identifier.scopus | eid_2-s2.0-85192845493 | - |
dc.identifier.volume | 384 | - |
dc.identifier.issue | 6696 | - |
dc.identifier.spage | eadm7168 | - |
dc.identifier.eissn | 1095-9203 | - |
dc.identifier.issnl | 0036-8075 | - |