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Abstract #1129

Improving FLAIR SAR efficiency by predicting B1-maps at 7T from a standard localizer scan using deep convolutional neural networks

Steffen Bollmann1, Samuel Kelly1, Viktor Vegh1, Anders Rodell2, Yas Tesiram1, Markus Barth1, and Kieran O’Brien2

1Centre for Advanced Imaging, The University of Queensland, Brisbane, Australia, 2Siemens Healthcare Pty Ltd., Brisbane, Australia

Ultra-high-field (7T) instrumentation offers the possibility of acquiring FLAIR images at an improved resolution when challenges such as efficient B1 calibration and SAR reductions can be realized. Instead of acquiring a separate B1-map, we propose to predict B1-maps based on the implicit B1 inhomogeneity field present in an AutoAlign localizer using deep convolutional neural networks. We show that a 34% reduction in SAR can be achieved by adjusting the power of FLAIR's adiabatic inversion pulse on a slice-by-slice basis using the B1 information without degradation of image quality.

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