This repository accompanies the paper
Abbasi‐Rad, Shahrokh, Kieran O’Brien, Samuel Kelly, Viktor Vegh, Anders Rodell, Yasvir Tesiram, Jin Jin, Markus Barth, and Steffen Bollmann. ‘Improving FLAIR SAR Efficiency at 7T by Adaptive Tailoring of Adiabatic Pulse Power through Deep Learning Estimation’. Magnetic Resonance in Medicine n/a, no. n/a (2020). https://doi.org/10.1002/mrm.28590.
preprint: Abbasi-Rad, S., O’Brien, K., Kelly, S., Vegh, V., Rodell, A., Tesiram, Y., Jin, J., Barth, M., Bollmann, S., 2019. Improving FLAIR SAR efficiency at 7T by adaptive tailoring of adiabatic pulse power using deep convolutional neural networks. arXiv:1911.08118 [physics]. http://arxiv.org/abs/1911.08118
The data is stored on OSF: https://osf.io/y5cq9/
The code is in an interactive notebook that can be directly executed in the browser on google colab or on a local computer using jupyter: https://colab.research.google.com/drive/1EOg5r30w0NtXJvTFdGhlR0ysGfaEaTpP