磁共振成像
核磁共振
共振(粒子物理)
物理
医学
原子物理学
放射科
作者
Yujiao Zhao,Ye Ding,Vick Lau,Christopher Man,Shi Su,Linfang Xiao,Alex T. L. Leong,EX Wu
出处
期刊:Science
[American Association for the Advancement of Science (AAAS)]
日期:2024-05-10
卷期号:384 (6696)
被引量:10
标识
DOI:10.1126/science.adm7168
摘要
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.
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