Abstract #0683

Investigating the robustness of convolutional neural network based B1+ prediction from localizer scans for SAR efficient 7T FLAIR imaging

Shahrokh Abbasi-Rad¹, Kieran O'Brien^1,2,3, Samuel Kelly¹, Viktor Vegh^1,3, Anders Rodell², Yasvir Tesiram¹, Jin Jin^2,3,4,5, Markus Barth^1,3,4, and Steffen Bollmann^1,3

¹Centre for Advanced Imaging, University of Queensland, Brisbane, Australia, ²Siemens Healthcare Pty Ltd, Brisbane, Australia, ³ARC Centre for Innovation in Biomedical Imaging Technology, University of Queensland, Brisbane, Australia, ⁴School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, Australia, ⁵Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, Los Angeles, CA, United States

In 7T MRI adiabatic pulses enable robust inversion of spins at the cost of increased SAR and longer scan times. A convolutional neural network was used to estimate the B₁⁺ profile from a localizer scan, Bloch equation simulations were used to calculate the required B₁⁺ for adiabaticity, and adiabatic pulse power was scaled accordingly reducing SAR by up to 38%. We investigated the robustness and efficiency of this approach and showed a substantial SAR reduction is possible without an additional B1 map acquisition. This resulted in an up to 27% faster T2-FLAIR acquisition with full brain coverage.

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