Back
Advances in MRzero: supervised learning of parallel imaging sequences including joint non-Cartesian trajectory and flip angle optimization
{Recently, MRzero has been proposed as a fully differentiable Bloch-equation-based MRI sequence invention framework. In this work, the approach is extended by parallel imaging capability, employing a CG SENSE reconstruction that allows optimizing for non-Cartesian sampling trajectories simultaneously with other sequence parameters like RF pulses and timings. The approach is tested herein by simulations on an in silico brain phantom and is found to yield improved reconstructions compared to regular Cartesian undersampling, and to simultaneously find variable flip angle patterns that compensate for transient signal induced blurring}
@misc{item_3319832, title = {{Advances in MRzero: supervised learning of parallel imaging sequences including joint non-Cartesian trajectory and flip angle optimization}}, booktitle = {{2021 ISMRM \& SMRT Annual Meeting \& Exhibition (ISMRM 2021)}}, abstract = {{Recently, MRzero has been proposed as a fully differentiable Bloch-equation-based MRI sequence invention framework. In this work, the approach is extended by parallel imaging capability, employing a CG SENSE reconstruction that allows optimizing for non-Cartesian sampling trajectories simultaneously with other sequence parameters like RF pulses and timings. The approach is tested herein by simulations on an in silico brain phantom and is found to yield improved reconstructions compared to regular Cartesian undersampling, and to simultaneously find variable flip angle patterns that compensate for transient signal induced blurring}}, year = {2021}, slug = {item_3319832}, author = {Glang, F and Loktyushin, A and Herz, K and Dang, HN and Deshmane, A and Weinm\"uller, S and Doerfler, A and Maier, A and Sch\"olkopf, B and Scheffler, K and Zaiss, M} }