Machine learning enables cheaper and safer low-power magnetic resonance imaging (MRI) without sacrificing accuracy, according to a new study.
May 10 2024American Association for the Advancement of Science Machine learning enables cheaper and safer low-power magnetic resonance imaging without sacrificing accuracy, according to a new study. According to the authors, these advances pave the way for affordable, patient-centric, and deep learning -powered ultra-low-field MRI scanners, addressing unmet clinical needs in diverse healthcare settings worldwide.
Magnetic Resonance Imaging has revolutionized healthcare, offering noninvasive and radiation-free imaging. It holds immense promise for advancing medical diagnoses through artificial intelligence. However, despite its five decades of development, MRI remains largely inaccessible, particularly in low- and middle-income countries. This is primarily due to the high costs associated with standard superconducting MRI scanners and the specialized infrastructure required for their operation.
To address MRI accessibility challenges, Yujiao Zhao and colleagues present a low-power and highly simplified ULF MRI scanner that operates on a standard wall power outlet and without the need for RF or magnetic shielding. The scanner uses a compact 0.05 Tesla magnet and incorporates active sensing and deep learning to address electromagnetic interference signals and improve image quality.
Related StoriesZhao et al. conducted imaging on healthy volunteers and show that the device was able to produce clear and detailed imaging on par with that obtained by high-power MRI devices currently used in the clinic. In a related Perspective, Udunna Anazodo and Stefan du Plessis note the limitations and challenges that need to be addressed before low-field MRI can be widely applied for clinical use.
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