EMI Cancellation for Shielding-Free Ultra-Low-Field MRI

Clinical Snapshot

Key Findings

• P Value: Not reported in abstract

• Effect Size: SNR improved from 2.35 dB (unshielded, no suppression) to 17.63 dB (EMIC-Net applied), representing a 15.28 dB gain. Image quality reported to approach shielded acquisition performance and surpass both EDITER and standard CNN comparators on PSNR and SSIM metrics.

• Primary Outcome: Electromagnetic interference suppression efficacy in unshielded 68 mT ULF MRI, measured by image SNR restoration during in vivo human brain imaging

• Nnt Or Sensitivity: Not applicable for this technical feasibility study. Key technical metric: SNR gain of 15.28 dB (approximately 7-fold amplitude improvement). Optimal configuration identified as three EMI sensing coils. Model demonstrated data efficiency with effective training on a small dataset (specific n not reported).

• Confidence Interval: Not reported

Clinical Application

Technical feasibility is demonstrated at proof-of-concept level. Clinical deployment requires: (1) prospective validation with adequate sample sizes and diverse EMI environments; (2) demonstration of diagnostic accuracy for clinically relevant pathologies; (3) integration with clinical workflows; (4) assessment of computational infrastructure requirements for real-time processing; (5) regulatory approval. The requirement for only three EMI sensing coils and a small training dataset are encouraging for practical implementation. Australia has a compelling use case for portable ULF MRI given its vast geographic area and significant rural and remote healthcare access disparities. The Royal Flying Doctor Service and remote Aboriginal and Torres Strait Islander community health services represent priority deployment contexts. The TGA would require clinical validation data demonstrating diagnostic safety before approval as a Class IIb or Class III medical device. There is currently no PBS MRI item number applicable to ULF portable systems, and RACGP guidelines do not yet address point-of-care MRI. The Australian Government's ongoing investment in rural health infrastructure (e.g., the Rural Health Multidisciplinary Training program) aligns with the technology's stated goals. However, Australian hospital EMI environments (particularly in older rural facilities) would require site-specific validation of the EMIC-Net model. Patients requiring brain MRI in resource-limited, remote, or emergency settings where conventional shielded MRI is unavailable or impractical. Potential applications include bedside neurological assessment in ICU, point-of-care imaging in rural and remote communities, and low-resource healthcare environments in low-to-middle-income countries.

Abstract

OBJECTIVE: Ultra-Low-Field Magnetic Resonance Imaging (ULF MRI) offers low cost and portability but suffers from electromagnetic interference (EMI) in unshielded environments. This study developed a deep learning-based active EMI suppression method to overcome these limitations. METHODS: Using a 68mT ULF MRI system, human body coupling was identified as a primary EMI pathway. We proposed EMIC-Net, a U-Net architecture incorporating Transformer and hybrid attention mechanisms, to learn the data-driven nonlinear mapping from sensing coil signals to radio-frequency (RF) receiver coil interference. Acquired data underwent phase and gain compensation prior to model training. The model's efficacy was validated through in vivo human brain imaging, comparing its performance with EDITER and standard CNN methods, and by assessing the impact of varying EMI coil numbers and training data volumes. RESULTS: EMIC-Net effectively suppressed complex dynamic EMI. It restored image SNR from 2.35 dB to 17.63 dB, with significant PSNR and SSIM improvements. Image quality neared shielded acquisitions and surpassed comparative methods. Three EMI coils provided optimal balance, and the model showed data efficiency, requiring a small dataset for effective training. CONCLUSION: The EMIC-Net method accurately predicts and efficiently removes EMI in unshielded ULF MRI, offering superior performance and practicality. SIGNIFICANCE: This research promotes portable, low-cost ULF MRI for primary healthcare and bedside diagnosis. It also offers insights for mitigating complex EMI issues in other RF sensing domains.

References

1. 1.Qiao, S., Yu, Y., Lin, T., Jiang, J., Liu, Y., & Li, X. (2025). EMI cancellation for shielding-free ultra-low-field MRI. IEEE Transactions on Biomedical Engineering. Advance online publication. https://doi.org/10.1109/TBME.2025.3635911