This work is the first to apply deep learning to the reconstruction of images encoded with nonlinear gradients. We apply a model-based deep learning network (MoDL) to simulated FRONSAC images and compare these to a PSF-based matrix inversion as well as cg-SENSE. The MoDL based reconstruction did not significantly change the behavior of signal noise. However, results demonstrate that the model-based deep learning network can outperform traditional reconstruction methods at high undersampling factors. Simulations also suggests that the regularizing network has potential to correct for miscalibration in the nonlinear gradient trajectory.
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