A Review of Electromagnetic Elimination Methods for low-field portable MRI scanner

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.