Rapid estimation of 2D relative B1+-maps from localizers in the human heart at 7T using deep learning

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Abstract

Purpose: Subject-tailored parallel transmission pulses for ultra-high fields body applications are typically calculated based on subject-specific (Formula presented.) -maps of all transmit channels, which require lengthy adjustment times. This study investigates the feasibility of using deep learning to estimate complex, channel-wise, relative 2D (Formula presented.) -maps from a single gradient echo localizer to overcome long calibration times. Methods: 126 channel-wise, complex, relative 2D (Formula presented.) -maps of the human heart from 44 subjects were acquired at 7T using a Cartesian, cardiac gradient-echo sequence obtained under breath-hold to create a library for network training and cross-validation. The deep learning predicted maps were qualitatively compared to the ground truth. Phase-only (Formula presented.) -shimming was subsequently performed on the estimated (Formula presented.) -maps for a region of interest covering the heart. The proposed network was applied at 7T to 3 unseen test subjects. Results: The deep learning-based (Formula presented.) -maps, derived in approximately 0.2 seconds, match the ground truth for the magnitude and phase. The static, phase-only pulse design performs best when maximizing the mean transmission efficiency. In-vivo application of the proposed network to unseen subjects demonstrates the feasibility of this approach: the network yields predicted (Formula presented.) -maps comparable to the acquired ground truth and anatomical scans reflect the resulting (Formula presented.) -pattern using the deep learning-based maps. Conclusion: The feasibility of estimating 2D relative (Formula presented.) -maps from initial localizer scans of the human heart at 7T using deep learning is successfully demonstrated. Because the technique requires only sub-seconds to derive channel-wise (Formula presented.) -maps, it offers high potential for advancing clinical body imaging at ultra-high fields.

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Krueger, F., Aigner, C. S., Hammernik, K., Dietrich, S., Lutz, M., Schulz-Menger, J., … Schmitter, S. (2023). Rapid estimation of 2D relative B1+-maps from localizers in the human heart at 7T using deep learning. Magnetic Resonance in Medicine, 89(3), 1002–1015. https://doi.org/10.1002/mrm.29510

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