Abstract #2812
Generalized Framework for Homogeneous Ultra-High-Field Brain Imaging
Patrick Liebig1, Juergen Herrler2, Raphael Tomi-Tricot3,4,5, Sydney Williams6, Belinda Ding-Yuan3,6, Majd Hlou7, Venkata Chebrolu8, Fasil Gadjimuradov7,9, Tom Hilbert10,11,12, Tobias Kober10,11,12, Rene Gumbrecht1, Robin Martin Heidemann1, Thomas Benkert7, Chris Rodgers13, David Andrew Porter6, Iulius Dragonu3, Armin Nagel14,15, and Shaihan Malik4,5
1Siemens Healthcare GmbH, Erlangen, Germany, 2Department of Neuroradiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany, 3MR Research Collaborations, Siemens Healthcare Limited, London, United Kingdom, 4Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom, 5Center for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas’ Hospital, London, United Kingdom, 6Imaging Centre of Excellence, University of Glasgow, Glasgow, United Kingdom, 7MR Applications Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany, 8Siemens Medical Solutions USA, Inc., Rochester, MN, United States, 9Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany, 10Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland, 11Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland, 12LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland, 13Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, United Kingdom, 14Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany, 15Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
Synopsis
7T MRI is affected by inhomogeneous transmit and receive B1 field that can impede the inherent gains in signal-to-noise ratio. pTx provides excellent results in correcting the transmit field and showed feasibility in a clinical setting as well1,2. Although multiple algorithms have been developed to correct for the receive profile or signal homogeneities in general, each algorithm has its own shortcomings. Here, we suggest combining prospective correction of the transmit field by pTx with a deep learning network to retrospectively correct for the remaining signal inhomogeneities (mainly receive field variations) in a generalized fashion.
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