Key result
Deep learning successfully estimated 2D relative B1+-maps from initial localizer scans of the human heart at 7T in approximately 0.2 seconds, matching the acquired ground truth.
Why the study?
Subject-tailored parallel transmission pulses at ultra-high fields rely on subject-specific transmit channel maps that require lengthy adjustment times, prompting evaluation of deep learning to rapidly estimate maps from localizers.
Does deep learning accurately estimate 2D relative B1+-maps from a single gradient echo localizer in the human heart at 7T compared to ground truth?
Does deep learning accurately estimate 2D relative B1+-maps from a single gradient echo localizer in the human heart at 7T compared to ground truth?
Deep learning can rapidly and accurately estimate 2D relative B1+-maps from initial localizer scans for 7T cardiac MRI, potentially reducing calibration times.
May shorten UHF cardiac pTx calibration; leaves open prospective validation for safe clinical use.
Purpose Subject‐tailored parallel transmission pulses for ultra‐high fields body applications are typically calculated based on subject‐specific ‐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 ‐maps from a single gradient echo localizer to overcome long calibration times. Methods 126 channel‐wise, complex, relative 2D ‐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 ‐shimming was subsequently performed on the estimated ‐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 ‐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 ‐maps comparable to the acquired ground truth and anatomical scans reflect the resulting ‐pattern using the deep learning‐based maps. Conclusion The feasibility of estimating 2D relative ‐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 ‐maps, it offers high potential for advancing clinical body imaging at ultra‐high fields.
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Krueger et al. (2022) studied Cardiac MRI at 7T (n=47). Deep learning-based estimation of 2D relative B1+-maps vs. Acquired ground truth B1+-maps was evaluated on Accuracy and derivation time of B1+-maps. Deep learning successfully estimated 2D relative B1+-maps from initial localizer scans of the human heart at 7T in approximately 0.2 seconds, matching the acquired ground truth.