低场便携式MRI扫描仪电磁消除方法综述
A Review of Electromagnetic Elimination Methods for low-field portable MRI scanner
AI总结：
本文综述了低场便携式MRI中电磁干扰消除的常规与深度学习方法，提出结合传统可靠性与深度学习优势的平衡方案以提升EMI抑制效果。
摘要：
本文分析了用于消除MRI系统中电磁干扰（EMI）的常规方法和深度学习方法。我们将传统的解析技术、自适应技术与先进的深度学习方法进行了比较，并重点指出了每种方法的主要优势和局限性。文中讨论了主动EMI消除的最新进展，例如外部EMI接收线圈，同时讨论了深度学习方法，这些方法通过利用在MRI数据上训练的神经网络，展现出更优越的EMI抑制能力。虽然深度学习改善了EMI消除效果和诊断能力，但它也带来了安全性和安保性方面的担忧，尤其是在商业应用中。本文提出了一种平衡的方法，将常规方法的可靠性与深度学习的先进能力相结合，以在MRI系统中实现更有效的EMI抑制。
英文摘要：
This paper analyzes conventional and deep learning methods for eliminating electromagnetic interference (EMI) in MRI systems. We compare traditional analytical and adaptive techniques with advanced deep learning approaches. Key strengths and limitations of each method are highlighted. Recent advancements in active EMI elimination, such as external EMI receiver coils, are discussed alongside deep learning methods, which show superior EMI suppression by leveraging neural networks trained on MRI data. While deep learning improves EMI elimination and diagnostic capabilities, it introduces security and safety concerns, particularly in commercial applications. A balanced approach, integrating conventional reliability with deep learning's advanced capabilities, is proposed for more effective EMI suppression in MRI systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
