Electromagnetic Noise Characterization and Suppression in Low-Field MRI Systems

TL;DR

This work addresses EMI and thermal-noise limitations in low-field MRI by introducing a practical, stepwise noise characterization and suppression protocol. Using a full experimental setup controlled by MaRCoS/MaRGE, the authors quantify noise as components are added, demonstrating that careful grounding, shielding, and cabling can bring the system within roughly 1.5× of the thermal noise floor even with a human subject. They validate the approach with quantitative noise measurements and representative in vivo images, linking EMI control to improved image quality. The protocol provides actionable guidance for system integration (e.g., gradient drivers, auto-TM) and advocates objective, absolute noise metrics to enable cross-platform comparisons and clinical translation.

Abstract

Purpose: Low-field MRI systems operate at single MHz-range frequencies, where signal losses are primarily dominated by thermal noise from the radio-frequency (RF) receive coils. Achieving operation close to this limit is essential for maximizing imaging performance and signal-to-noise ratio (SNR). However, electromagnetic interference (EMI) from cabling, electronics, and patient loading often degrades system performance. Our goal is to develop and validate a practical protocol that guides users in identifying and suppressing electromagnetic noise in low-field MRI systems, enabling operation near the thermal noise limit. Methods: We present a systematic, stepwise methodology that includes diagnostic measurements, hardware isolation strategies, and good practices for cabling and shielding. Each step is validated with corresponding noise measurements under increasingly complex system configurations, both unloaded and with a human subject present. Results: Noise levels were monitored through the incremental assembly of a low-field MRI system, revealing key sources of EMI and quantifying their impact. Final configurations achieved noise within 1.5x the theoretical thermal bound with a subject in the scanner. Image reconstructions illustrate the direct relationship between system noise and image quality. Conclusion: The proposed protocol enables low-field MRI systems to operate close to fundamental noise limits in realistic conditions. The framework also provides actionable guidance for the integration of additional system components, such as gradient drivers and automatic tuning networks, without compromising SNR.

Figures (3)

• Figure 1: Diagram of the main components in a low-field MRI scanner employing MaRCoS, including details and photographs of these components in the NextMRI system employed for this work. Figure adapted from Ref. Huang2025.
• Figure 2: Four-step protocol for electromagnetic noise characterization and suppression in low-field MRI systems. Steps 2–4 involve repeated measurements through stages I–VII to assess noise contributions at each level of system assembly.
• Figure 3: Comparison of in vivo images acquired under different EMI suppression configurations using a grounded conductive blanket. From left to right: (a) blanket wrapped around the subject, (b) blanket under the subject, (c) blanket wrapped around the subject, with the MaRCoS and TM boxes open to couple a discrete EMI, and (d) blanket wrapped around the subject, with a switched-mode power supply next to the gradient lines to couple 50 Hz noise. Noise measurements are shown also for reference. The top traces (yellow) correspond to the real time signals over the 50 ms acquisition. The bottom traces (red) correspond to the power spectra over the 50 kHz bandwidth. The axes scales are the same across all plots.