Deep learning-based whole-brain B1+-mapping at 7T

Abstract

Purpose: This study investigates the feasibility of using complex-valued neural networks (NNs) to estimate quantitative transmit magnetic RF field (B1+) maps from multi-slice localizer scans with different slice orientations in the human head at 7T, aiming to accelerate subject-specific B1+-calibration using parallel transmission (pTx). Methods: Datasets containing channel-wise B1+-maps and corresponding multi-slice localizers were acquired in axial, sagittal, and coronal orientation in 15 healthy subjects utilizing an eight-channel pTx transceiver head coil. Training included five-fold cross-validation for four network configurations: (Formula presented.) used transversal, (Formula presented.) sagittal, (Formula presented.) coronal data, and (Formula presented.) was trained on all slice orientations. The resulting maps were compared to B1+-reference scans using different quality metrics. The proposed network was applied in-vivo at 7T in two unseen test subjects using dynamic kt-point pulses. Results: Predicted B1+-maps demonstrated a high similarity with measured B1+-maps across multiple orientations. The estimation matched the reference with a mean relative error in the magnitude of (2.70 ± 2.86)% and mean absolute phase difference of (6.70 ± 1.99)° for transversal, (1.82 ± 0.69)% and (4.25 ± 1.62)° for sagittal ((Formula presented.)), as well as (1.33 ± 0.27)% and (2.66 ± 0.60)° for coronal slices ((Formula presented.)) considering brain tissue. (Formula presented.) trained on all orientations enables a robust prediction of B1+-maps across different orientations. Achieving a homogenous excitation over the whole brain for an in-vivo application displayed the approach's feasibility. Conclusion: This study demonstrates the feasibility of utilizing complex-valued NNs to estimate multi-slice B1+-maps in different slice orientations from localizer scans in the human brain at 7T.