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SNR Enhancement Considerations for Loop Receive Coils at Ultra-High Fields

Preprint Created on 19 Jun 2026 bioRxiv

Purpose: To quantify parasitic losses in ultra-high field (UHF) magnetic resonance imaging (MRI) receive coils and determine how they contribute to the mismatch between numerically predicted and experimentally realized signal-to-noise ratio (SNR), with the goal of guiding receive-array designs toward ultimate intrinsic SNR (uiSNR). Methods: SNR was measured across multiple field strengths (3T, 7T, 10.5T) using commercial and custom-built arrays. To quantify parasitic losses, unloaded-to-loaded quality factor ratio (QR) measurements were performed on representative loop resonators and practical RF coils. Measured losses were combined with single-loop electromagnetic simulations to separate conductor, radiation, and component losses. These bench-derived loss estimates were then incorporated into full-array electromagnetic simulations of a 128-channel receive array to evaluate their impact on predicted intrinsic SNR. Results: Measurements across field strengths supported the expected supralinear increase of SNR with B0. QR analysis showed that, at UHF, radiation loss must be excluded from unloaded-Q measurements to avoid overestimating electronic-noise penalties, and that multiple seemingly modest parasitic losses collectively impose substantial SNR degradation. In the 128-channel array, simulations including only conductor and radiation losses predicted 93% of central uiSNR, whereas inclusion of the full measured parasitic-loss budget reduced predicted performance to 78%, in close agreement with the experimentally measured 77%. Conclusions: The gap between predicted and realized SNR performance of high-channel-count 10.5T loop arrays can be largely explained by parasitic losses that are not captured in conventional simulations. A bench-measurement-informed simulation framework enables more realistic prediction of coil performance and provides practical guidance for optimizing future UHF receive arrays.

Lagore, R. L., Waks, M., Hasapopoulos, T., Mercer, T., Grant, A., Eryaman, Y., Ugurbil, K., Adriany, G., Sadeghi-Tarakameh, A.

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