Deep learning accelerated Sandwich for 3D multi-channel B1+ mapping at 7T in less than 10 seconds

Motivation: Online multi-channel B1+ mapping is essential in ultra-high field MRI for individual optimization of parallel transmit pulses. Goal(s): To rapidly and accurately estimate multi-channel B1+ maps at 7T. Approach: A deep learning reconstruction was implemented for accelerating the Sandwich sequence. The approach was validated by comparing the estimated 3D B1+ maps of five subjects with the fully sampled acquisitions and conventional compressed sensing reconstructions. Results: The proposed deep learning method generates accurate 3D multi-channel B1+ maps in under 10 seconds, with an RMSE of 3.6$$$\,$$$±$$$\,$$$1.4° for a target flip angle of 90°. Impact: This study demonstrates a deep learning-based method for rapid B1+ mapping in ultra-high field MRI, significantly reducing acquisition time to under 10 seconds while maintaining accuracy. The approach enhances the efficiency of parallel transmission, facilitating clinical applications at 7T.

Authors

Publication Details

Journal
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Published
2025-09-16
DOI
https://doi.org/10.58530/2025/2628
Primary Topic
Advanced MRI Techniques and Applications
Type
article
Field-Weighted Citation Impact
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article

Deep learning accelerated Sandwich for 3D multi-channel B1+ mapping at 7T in less than 10 seconds

Tom Hilbert, Aaron T. Hess, James Kent, Patrick Alexander Liebig et al.
Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Advanced MRI Techniques and Applications
article

Deep learning accelerated Sandwich for 3D multi-channel B1+ mapping at 7T in less than 10 seconds

Tom Hilbert, Aaron T. Hess, James Kent, Patrick Alexander Liebig, Dominik M. Nickel, Robin Martin Heidemann, Bénédicte Maréchal, Gian Franco Piredda, Jocelyn Philippe, Tommaso Di Noto, Jürgen Herrler, Thomas Yu, Antoine Klauser, Tobias Kober, Emilie Sleight, Lina Bacha, Natalia Pato Montemayor, Jean-Philippe Tiran
article en

Abstract

Motivation: Online multi-channel B1+ mapping is essential in ultra-high field MRI for individual optimization of parallel transmit pulses. Goal(s): To rapidly and accurately estimate multi-channel B1+ maps at 7T. Approach: A deep learning reconstruction was implemented for accelerating the Sandwich sequence. The approach was validated by comparing the estimated 3D B1+ maps of five subjects with the fully sampled acquisitions and conventional compressed sensing reconstructions. Results: The proposed deep learning method generates accurate 3D multi-channel B1+ maps in under 10 seconds, with an RMSE of 3.6$$$\,$$$±$$$\,$$$1.4° for a target flip angle of 90°. Impact: This study demonstrates a deep learning-based method for rapid B1+ mapping in ultra-high field MRI, significantly reducing acquisition time to under 10 seconds while maintaining accuracy. The approach enhances the efficiency of parallel transmission, facilitating clinical applications at 7T.

Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition
Openalex Percentile: Top 64%
Advanced MRI Techniques and Applications
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