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Abstract #0756

SmartPulse, a Machine Learning Approach for Calibration-Free Dynamic RF Shimming in Body Imaging

Raphaël Tomi-Tricot1, Vincent Gras1, Bertrand Thirion2, Franck Mauconduit3, Nicolas Boulant1, Hamza Cherkaoui2, Pierre Zerbib4, Alexandre Vignaud1, Alain Luciani4,5,6, and Alexis Amadon1

1NeuroSpin, CEA, Université Paris-Saclay, Gif-sur-Yvette, France, 2Parietal, Inria, Université Paris-Saclay, Gif-sur-Yvette, France, 3Siemens Heathcare SAS, Saint-Denis, France, 4Department of Radiology, AP-HP, CHU Henri Mondor, Créteil, France, 5Université Paris-Est Créteil Val-de-Marne, Créteil, France, 6INSERM Unité U955, Equipe 18, Créteil, France

At high field, tailored static or, better, dynamic RF shimming can be used to reduce artifacts due to transmit B1 field inhomogeneity, but those methods require extra time for calibration, which can disrupt clinical workflows. Recently, universal pulses (UP) were introduced in brain imaging to get rid of calibration. In this work, a machine learning method is proposed to extend universal pulse kT-point design to body imaging where inter-subject variability is more pronounced, by classifying subjects into one of several predefined categories. This method outperforms UP design, and yields images similar to those obtained with state-of-the-art tailored design.

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