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

• Published in: Magnetic resonance in medicine Vol. 89; no. 3; pp. 1002 - 1015
• Main Authors: Krueger, Felix, Aigner, Christoph Stefan, Hammernik, Kerstin, Dietrich, Sebastian, Lutz, Max, Schulz‐Menger, Jeanette, Schaeffter, Tobias, Schmitter, Sebastian
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc 01.03.2023
• Subjects: 7 Tesla; B1+$$ {\mathrm{B}}_1^{+} $$‐mapping; body MRI; Cartesian coordinates; Deep learning; Estimation; Feasibility studies; Heart; Image processing; parallel transmission; Transmission efficiency
• ISSN: 0740-3194; 1522-2594
• DOI: 10.1002/mrm.29510

Purpose Subject‐tailored parallel transmission pulses for ultra‐high fields body applications are typically calculated based on subject‐specific B1+$$ {\mathrm{B}}_1^{+} $$‐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 B1+$$ {\mathrm{B}}_1^{+} $$‐maps from a single gradient echo localizer to overcome long calibration times. Methods 126 channel‐wise, complex, relative 2D B1+$$ {\mathrm{B}}_1^{+} $$‐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 B1+$$ {\mathrm{B}}_1^{+} $$‐shimming was subsequently performed on the estimated B1+$$ {\mathrm{B}}_1^{+} $$‐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 B1+$$ {\mathrm{B}}_1^{+} $$‐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 B1+$$ {\mathrm{B}}_1^{+} $$‐maps comparable to the acquired ground truth and anatomical scans reflect the resulting B1+$$ {\mathrm{B}}_1^{+} $$‐pattern using the deep learning‐based maps. Conclusion The feasibility of estimating 2D relative B1+$$ {\mathrm{B}}_1^{+} $$‐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 B1+$$ {\mathrm{B}}_1^{+} $$‐maps, it offers high potential for advancing clinical body imaging at ultra‐high fields.