High Dynamic Range Turbo-FLASH B1 Mapping for Characterizing Multi-Transmit Multi-Receive RF Arrays, Validated at 7 T and 11.7 T.

Authors

Natalia Dudysheva, Franck Mauconduit, Michel Luong, Alexis Amadon, Nicolas Boulant, Vincent Gras

Published in

Magnetic resonance in medicine. Sep 17, 2026. Epub Sep 17, 2026.

Abstract

Transmit (B₁ ⁺) and receive (B₁ ^-) sensitivities of parallel-transmit/receive RF array are a prerequisite for modern ultra-high-field MRI. Their accurate mapping remains challenging for coils with high transmit dynamic range, particularly in regions with low combined-mode B₁ ⁺ magnitude, where standard reconstruction is impaired by noise. This work presents a new B₁ ⁺ and B₁ ^- model-based fitting approach for interferometric presaturated TurboFLASH (satTFL) data. With an optimized acquisition scheme, this method improves B₁ ⁺/B₁ ^- mapping accuracy.
Voxel-wise B₁ ⁺ and B₁ ^- are expressed as the product of a complex unit-norm vector (phasor) and a scalar (efficiency). Phasors are estimated through singular value decomposition of satTFL signals, and efficiencies are recovered with fitting to the signal equation. This method was evaluated in phantom simulations for 7 T and 11.7 T 8Tx/32Rx RF arrays and compared with standard approaches. As it supports a variable number of pre-saturated scans, different interferometric schemes were optimized for B₁ ⁺ accuracy. Finally, schemes yielding acceptable B₁ ⁺ error with minimal acquisition time were tested in vitro at 7 T and 11.7 T and in vivo at 7 T.
The proposed method provided lower median B₁ ⁺ and B₁ ^- errors than conventional satTFL methods and fewer outliers; the number of voxels with error > 30% is reduced from several dozens to zero. In vitro results were consistent with the simulations, and in vivo maps demonstrated good quality.
The proposed method improves B₁ ⁺ and B₁ ^- mapping robustness to noise and enables more flexible satTFL acquisition scheme, allowing better trade-offs between accuracy and scan time.

PMID:
42750484
Bibliographic data and abstract were imported from PubMed on 17 Sep 2026.

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