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

TL;DR

This paper reviews traditional and deep learning methods for electromagnetic interference elimination in low-field portable MRI scanners, highlighting recent advancements, comparing techniques, and discussing safety and effectiveness.

Contribution

It provides a comprehensive comparison of conventional and deep learning EMI elimination methods, proposing an integrated approach for improved MRI system performance.

Findings

Deep learning methods outperform traditional techniques in EMI suppression.

Active EMI elimination with external receiver coils enhances MRI image quality.

Deep learning introduces security and safety considerations in clinical applications.

Abstract

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.