Purpose: Receive array layout, noise mitigation, and B0 field strength are crucial contributors to SNR and parallel-imaging performance. Here, we investigate SNR and parallel-imaging gains at 10.5 T compared with 7 T using 32-channel receive arrays at both fields. Methods: A self-decoupled 32-channel receive array for human brain imaging at 10.5 T (10.5T-32Rx), consisting of 31 loops and one cloverleaf element, was co-designed and built in tandem with a 16-channel dual-row loop transmitter. Novel receive array design and self-decoupling techniques were implemented. Parallel imaging performance, in terms of SNR and noise amplification (g-factor), of the 10.5T-32Rx was compared with the performance of an industry-standard 32-channel receiver at 7 T (7T-32Rx) through experimental phantom measurements. Results: Compared with the 7T-32Rx, the 10.5T-32Rx provided 1.46 times the central SNR and 2.08 times the peripheral SNR. Minimum inverse g-factor value of the 10.5T-32Rx (min[1/g] = 0.56) was 51% higher than that of the 7T-32Rx (min[1/g] = 0.37) with R = 4 × 4 2D acceleration, resulting in significantly enhanced parallel-imaging performance at 10.5 T compared with 7 T. The g-factor values of 10.5 T-32 Rx were on par with those of a 64-channel receiver at 7 T (eg, 1.8 vs 1.9, respectively, with R = 4 × 4 axial acceleration). Conclusion: Experimental measurements demonstrated effective self-decoupling of the receive array as well as substantial gains in SNR and parallel-imaging performance at 10.5 T compared with 7 T.
Bibliographical noteFunding Information:
National Institutes of Health (U01EB025144, P41 EB027061, NIH S10 RR029672, P30 NS076408, and NIH R34AG055178) The authors thank Myung Kyun Woo, Lance DelaBarre, Yigitcan Eryaman, and Matt Waks (all with the Center for Magnetic Resonance Research, University of Minnesota, Minneapolis) for the constructive feedback.
© 2021 International Society for Magnetic Resonance in Medicine
- RF coils
- noise correlation
- parallel imaging
- receive array
- ultrahigh-field MRI
PubMed: MeSH publication types
- Journal Article
- Research Support, N.I.H., Extramural