Deep learning-based whole-brain B -mapping at 7T.

Journal: Magnetic resonance in medicine

PMID: 39462473

Abstract

PURPOSE: This study investigates the feasibility of using complex-valued neural networks (NNs) to estimate quantitative transmit magnetic RF field (B ) maps from multi-slice localizer scans with different slice orientations in the human head at 7T, aiming to accelerate subject-specific B -calibration using parallel transmission (pTx).

Authors

Felix Krueger

Physikalisch-Technische Bundesanstalt, Braunschweig and Berlin, Germany.
Christoph Stefan Aigner

Physikalisch-Technische Bundesanstalt, Braunschweig and Berlin, Germany.
Max Lutz

Physikalisch-Technische Bundesanstalt, Braunschweig and Berlin, Germany.
Layla Tabea Riemann

Physikalisch-Technische Bundesanstalt, Berlin, Germany.
Katja Degenhardt

Physikalisch-Technische Bundesanstalt, Berlin, Germany.
Kimon Hadjikiriakos

Physikalisch-Technische Bundesanstalt, Berlin, Germany.
Felix Frederik Zimmermann

Physikalisch-Technische Bundesanstalt, Berlin, Germany.
Kerstin Hammernik

Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria.
Jeanette Schulz-Menger

Charité-Universitätsmedizin Berlin, Experimental and Clinical Research Center, Working Group On CMR and HELIOS Klinikum Berlin Buch, Cardiology Berlin, DZHK partnersite Berlin, Berlin, Germany.
Tobias Schaeffter
Sebastian Schmitter

Physikalisch-Technische Bundesanstalt, Braunschweig and Berlin, Germany.

Keywords

Adult Algorithms Brain Brain Mapping Deep Learning Female Healthy Volunteers Humans Image Processing, Computer-Assisted Magnetic Resonance Imaging Male Neural Networks, Computer Reproducibility of Results Young Adult

External Resources

View on PubMed Access via DOI PubMed (39462473)

Deep learning-based whole-brain B -mapping at 7T.

Abstract

Authors

Keywords

External Resources

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