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Offset Sampling Improves Deep Learning based Accelerated MRI Reconstructions by Exploiting Symmetry
Aaron Defazio
Deep learning approaches to accelerated MRI take a matrix of sampled Fourier-space lines as input and produce a spatial image as output. In this work we show that by careful choice…
GrappaNet: Combining Parallel Imaging with Deep Learning for Multi-Coil MRI Reconstruction
Anuroop Sriram, Jure Zbontar, Tullie Murrell +3
Magnetic Resonance Image (MRI) acquisition is an inherently slow process which has spurred the development of two different acceleration methods: acquiring multiple correlated samp…
Beyond Folklore: A Scaling Calculus for the Design and Initialization of ReLU Networks
Aaron Defazio, Léon Bottou
We propose a system for calculating a "scaling constant" for layers and weights of neural networks. We relate this scaling constant to two important quantities that relate to the o…
Methods of interpreting error estimates for grayscale image reconstructions
Aaron Defazio, Mark Tygert
One representation of possible errors in a grayscale image reconstruction is as another grayscale image estimating potentially worrisome differences between the reconstruction and…