标题： 低场磁共振成像：利用机器学习的力量 显示英文标题

标题： MR imaging in the low-field: Leveraging the power of machine learning

摘要： 最近在磁共振成像（MRI）硬件和软件方面的创新重新激发了对低场（$<1\,\mathrm{T}$）和超低场MRI（$<0.1\,\mathrm{T}$）的兴趣。 这些技术具有较低的功耗、减少的特定吸收率、减少的磁场不均匀性以及成本效益，为资源有限和现场护理环境提供了有前景的替代方案。 然而，低场MRI面临固有的挑战，如信噪比降低，因此可能导致空间分辨率较低或扫描时间较长。 本章探讨了低场和超低场MRI的挑战和机遇，重点介绍了机器学习（ML）在克服这些限制中的作用。 我们概述了深度神经网络及其在提高低场和超低场MRI性能中的应用。 讨论了基于特定机器学习的解决方案，包括先进的图像重建、去噪和超分辨率算法。 本章最后探讨了将机器学习与低场MRI相结合如何扩展其临床应用并提高可及性，可能在各种医疗环境中彻底改变其应用。 显示英文摘要

摘要： Recent innovations in Magnetic Resonance Imaging (MRI) hardware and software have reignited interest in low-field ($<1\,\mathrm{T}$) and ultra-low-field MRI ($<0.1\,\mathrm{T}$). These technologies offer advantages such as lower power consumption, reduced specific absorption rate, reduced field-inhomogeneities, and cost-effectiveness, presenting a promising alternative for resource-limited and point-of-care settings. However, low-field MRI faces inherent challenges like reduced signal-to-noise ratio and therefore, potentially lower spatial resolution or longer scan times. This chapter examines the challenges and opportunities of low-field and ultra-low-field MRI, with a focus on the role of machine learning (ML) in overcoming these limitations. We provide an overview of deep neural networks and their application in enhancing low-field and ultra-low-field MRI performance. Specific ML-based solutions, including advanced image reconstruction, denoising, and super-resolution algorithms, are discussed. The chapter concludes by exploring how integrating ML with low-field MRI could expand its clinical applications and improve accessibility, potentially revolutionizing its use in diverse healthcare settings.