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Abstract #0953

A Machine-Learning Approach for Saturation-Prepared Turbo FLASH B1+ Maps Calibration

Michael Dubiner1, Jan Sedlacik1,2,3,4, Tom Wilkinson1,2,3, Pip Bridgen1,2,3, Franck Mauconduit5, Alexis Amadon5, Sharon Giles1,2,3, Radhouene Neji1,3,6, Joseph V. Hajnal1,2,3, Shaihan J. Malik1,2,3, and Raphael Tomi-Tricot1,2,3,6
1Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom, 2Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom, 3London Collaborative Ultra high field System (LoCUS), London, United Kingdom, 4Great Ormond Street Hospital for Children, London, United Kingdom, 5Paris-Saclay University, CEA, CNRS, BAOBAB, NeuroSpin, Gif-sur-Yvette, France, 6MR Research Collaborations, Siemens Healthcare Limited, Frimley, United Kingdom

Synopsis

Keywords: RF Pulse Design & Fields, High-Field MRIA fast B1+ mapping sequence such as saturation-prepared turbo FLASH (satTFL) is desirable for online pulse design at ultra-high field, but it often results in inaccuracies. Previous work performed linear fitting of the B1+ magnitude obtained with the satTFL over that of the longer but more accurate Actual Flip angle Imaging (AFI) sequence to obtain calibration parameters that would correct the satTFL B1+ on the fly for brain imaging. In this work we introduce a new machine-learning-based method that uses additional features to create a more precise and less location-dependent accuracy.

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Keywords