ArticleMagnetic resonance in medicine2026
Electromagnetic Noise Characterization and Suppression in Low-Field MRI Systems.
Article in Magnetic resonance in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed.
- Qualitative and quantitative hard-tissue MRI with portable Halbach scanners.Scientific reports · 2026Article
- In Vivo Imaging With a Low-Cost MRI Scanner and Cloud Data Processing in Low-Resource Settings.NMR in biomedicine · 2026Article
- FENCE: Flexible Electric Noise Reduction Endo-Shield for the Suppression of Electromagnetic Interference in Low-Field MRI.NMR in biomedicine · 2026Article
- Subject grounding to reduce electromagnetic interference for MRI scanners operating in unshielded environments.Magnetic resonance in medicine · 2026Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
purposeOur 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.
methodsWe 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.
resultsNoise 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 1.5
conclusionThe 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 signal-to-noise ratio (SNR).
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Registered trials
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