A Review of Electromagnetic Elimination Methods for low-field portable MRI scanner
This addresses EMI elimination in MRI systems, particularly for low-field portable scanners, but is an incremental review paper.
This paper reviews electromagnetic interference (EMI) elimination methods for MRI systems, comparing traditional analytical/adaptive techniques with deep learning approaches that show superior EMI suppression by leveraging neural networks trained on MRI data. It proposes a balanced integration of conventional reliability with deep learning's capabilities for more effective EMI suppression.
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.