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.

中文翻译：

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0.05 特斯拉全身磁共振成像

尽管取得了半个世纪的进步，但全球磁共振成像 (MRI) 的可及性仍然有限且参差不齐，阻碍了其在医疗保健领域的全部潜力。最初，MRI 的开发集中在 0.05 特斯拉左右的低场，但在 1983 年推出 1.5 特斯拉全身超导扫描仪后，进展停止了。利用 0.05 特斯拉永久磁铁和深度学习消除电磁干扰，我们开发了一种全身超导扫描仪。使用标准墙壁电源插座运行且无射频和磁屏蔽的扫描仪。我们展示了其对各种解剖结构成像的广泛适用性。此外，我们开发了三维深度学习重建，通过利用大量高场 MRI 数据来提高图像质量。这些进步为经济实惠的深度学习驱动的超低场 MRI 扫描仪铺平了道路，解决了全球不同医疗保健环境中未满足的临床需求。