Back
MRzero sequence generation using analytic signal equations as forward model and neural network reconstruction for efficient auto-encoding
{MRzero is a fully differentiable Bloch-equation-based MRI sequence invention framework. Instead of using time-consuming average-isochromat-based Bloch simulations, analytic signal equations are used as alternative forward differentiable MR scan simulation method. Neural network reconstruction is used for efficient auto-encoding. The joint optimization of sequence and NN parameters for B1 and T1 mapping can be performed 2 to 3 orders of magnitude faster then in previous MRzero approaches. The optimized sequence is tested by measurements in vivo at 3T and compared to a standard inversion recovery. High quality B1 and T1 maps are provided with less total acquisition time and energy deposition.}
@misc{item_3319849, title = {{MRzero sequence generation using analytic signal equations as forward model and neural network reconstruction for efficient auto-encoding}}, booktitle = {{2021 ISMRM \& SMRT Annual Meeting \& Exhibition (ISMRM 2021)}}, abstract = {{MRzero is a fully differentiable Bloch-equation-based MRI sequence invention framework. Instead of using time-consuming average-isochromat-based Bloch simulations, analytic signal equations are used as alternative forward differentiable MR scan simulation method. Neural network reconstruction is used for efficient auto-encoding. The joint optimization of sequence and NN parameters for B1 and T1 mapping can be performed 2 to 3 orders of magnitude faster then in previous MRzero approaches. The optimized sequence is tested by measurements in vivo at 3T and compared to a standard inversion recovery. High quality B1 and T1 maps are provided with less total acquisition time and energy deposition.}}, year = {2021}, slug = {item_3319849}, author = {Weinm\"uller, S and Dang, HN and Loktyushin, A and Glang, F and Doerfler, A and Maier, A and Sch\"olkopf, B and Scheffler, K and Zaiss, M} }