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Abstract #0848

Deep Learning Reconstruction for FRONSAC

Zhehong Zhang1 and Gigi Galiana2
1Department of Biomedical Engineering, Yale University, New Haven, CT, United States, 2Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States


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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