This paper presents a comprehensive analysis of both conventional and deep
learning methods for eliminating electromagnetic interference (EMI) in MRI
systems. We explore the underlying principles and implementation of traditional
analytical and adaptive EMI elimination techniques, as well as cutting-edge
deep learning approaches. Through a detailed comparison, the strengths and
limitations of each method are highlighted. Recent advancements in active EMI
elimination utilizing multiple external EMI receiver coils and analytical
techniques are discussed alongside the superior performance of deep learning
methods, which leverage neural networks trained on extensive MRI data. While
deep learning methods demonstrate significant improvements in EMI suppression,
enhancing diagnostic capabilities and accessibility of MRI technology, they
also introduce potential security and safety concerns, especially in production
and commercial applications. This study underscores the need to address these
challenges to fully realize the benefits of deep learning in EMI elimination.
The findings suggest a balanced approach, combining the reliability of
conventional methods with the advanced capabilities of deep learning, to
develop more robust and effective EMI suppression strategies in MRI systems.

综述了传统方法和深度学习方法在消除磁共振成像系统中的电磁干扰方面的综合分析和实施，并通过详细比较强调了每种方法的优势和局限性。研究结果表明，结合传统方法的可靠性和深度学习的先进能力，开发更健壮、有效的电磁干扰抑制策略在 MRI 系统中是有必要的。