Rapid estimation of 2D relative B 1 + -maps from localizers in the human heart at 7T using deep learning

• Published in: Magnetic resonance in medicine 2023-03, Vol.89 (3), p.1002-1015
• Main Authors: Krueger, Felix, Aigner, Christoph Stefan, Hammernik, Kerstin, Dietrich, Sebastian, Lutz, Max, Schulz-Menger, Jeanette, Schaeffter, Tobias, Schmitter, Sebastian
• Format: Article
• Language: English
• Subjects: Calibration; Deep Learning; Heart - diagnostic imaging; Humans; Image Interpretation, Computer-Assisted - methods; Magnetic Resonance Imaging - methods
• ISSN: 1522-2594
• DOI: 10.1002/mrm.29510

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. 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. 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. 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.