Felix Krüger1, Max Lutz1, Christoph Stefan Aigner1, and Sebastian Schmitter1
1Physikalisch-Technische Bundesanstalt, Braunschweig and Berlin, Germany
1Physikalisch-Technische Bundesanstalt, Braunschweig and Berlin, Germany
We utilize a residual neural network for the
design of slice selective RF and gradient trajectories. The network was trained
with 300k SLR RF pulses. It predicts the RF pulse and the gradient for a desired
magnetization profile. We evaluate the feasibility and limitations of this new
approach.
Figure 3: The NRMSE over the whole parameters space. In blue the results for the Network I trained
on 1D slice-selective SLR pulses for different validation data sets are shown.
In red the same principle was extended to Network II, for which SMS pulses were
included in the library. The first row ((a)-(c) displays the influence of the BWTP, the second row ((d)-(f)) with respect to the FA and the
third row ((g)-(i)) to a varying slice thickness
. The last row ((j)-(l)) shows the influence of
the slice shift
. The relations are evaluated for the
magnetization profile, the RF pulse and the gradient.
Figure 2: Representative example for a validation data set
with a BWTP of 7, FA of 45°, slice thickness of 7mm and no slice shift used on the
pretrained neural network is shown. In (e), (f), (g) the output is compared to the
ground truth. The prediction is used for a second Bloch simulation to analyze
the difference between the desired and the predicted magnetization in (a), (b), (c), (d). The predicted result is in good agreement with the ground truth for
this parameter set.
