神经影像学
计算机科学
磁共振成像
领域(数学)
一套
深度学习
医学物理学
人工智能
医学
神经科学
放射科
心理学
数学
历史
考古
纯数学
作者
W. Taylor Kimberly,Annabel Sorby‐Adams,Andrew Webb,EX Wu,Rachel Beekman,Ritvij Bowry,Steven J. Schiff,Adam de Havenon,Francis X. Shen,Gordon Sze,Pamela W. Schaefer,Juan Eugenio Iglesias,Matthew S. Rosen,Kevin N. Sheth
标识
DOI:10.1038/s44222-023-00086-w
摘要
The advent of portable, low-field MRI (LF-MRI) heralds new opportunities in neuroimaging. Low power requirements and transportability have enabled scanning outside the controlled environment of a conventional MRI suite, enhancing access to neuroimaging for indications that are not well suited to existing technologies. Maximizing the information extracted from the reduced signal-to-noise ratio of LF-MRI is crucial to developing clinically useful diagnostic images. Progress in electromagnetic noise cancellation and machine learning reconstruction algorithms from sparse k-space data as well as new approaches to image enhancement have now enabled these advancements. Coupling technological innovation with bedside imaging creates new prospects in visualizing the healthy brain and detecting acute and chronic pathological changes. Ongoing development of hardware, improvements in pulse sequences and image reconstruction, and validation of clinical utility will continue to accelerate this field. As further innovation occurs, portable LF-MRI will facilitate the democratization of MRI and create new applications not previously feasible with conventional systems. The advent of portable, low-field MRI is transforming clinical brain imaging. This Review discusses the bioengineering advances that have enabled scanning outside the controlled environment of conventional MRI suites, enhancing access to neuroimaging. Ongoing development and innovation will increase the real-world application of MRI.
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