用机器学习提升低场磁共振成像的图像质量与效率
MR imaging in the low-field: Leveraging the power of machine learning
- 用深度神经网络改进低场MRI的图像重建与降噪
- 实现低场下高分辨率成像，克服信噪比低的瓶颈
- 适合医疗资源匮乏地区及便携式设备应用
近期磁共振成像（MRI）软硬件创新重新激发了对低场（<1 T）和超低场MRI（<0.1 T）的兴趣。这类技术具有功耗低、射频吸收率小、磁场不均性弱和成本低等优势，适用于资源有限和床旁诊疗场景。然而，低场MRI面临信噪比低的问题，可能导致空间分辨率下降或扫描时间延长。本文综述了低场与超低场MRI的挑战与机遇，重点探讨机器学习（ML）在克服这些限制中的作用。介绍了深度神经网络在图像重建、去噪和超分辨率等任务中的应用。研究表明，将机器学习与低场MRI结合可显著提升成像性能，拓展其临床应用范围，增强医疗可及性，有望在多样化的医疗环境中实现变革。
原文摘要 · Abstract (English)
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.
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