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Abstract #4785

ShiftNets: Deep Convolutional Neural Networks for MR Image Reconstruction & the Importance of Receptive Field of View

Philip K. Lee1,2, Makai Mann1, and Brian A. Hargreaves1,2,3

1Electrical Engineering, Stanford University, Stanford, CA, United States, 2Radiology, Stanford University, Stanford, CA, United States, 3Bioengineering, Stanford University, Stanford, CA, United States

Deep learning has been applied to the Parallel Imaging problem of resolving coherent aliasing in image domain. Convolutional neural networks have finite receptive FOV, where each output pixel is a function of a limited number of input pixels. For uniformly undersampled data, a simple hypothesis is that including the aliased peak in the receptive FOV would improve suppression of aliasing. We show that a simple channel augmentation scheme allows us to resolve aliasing using 50x fewer parameters than a large U-Net with millions of parameters and a global receptive FOV. This method was tested on retrospectively undersampled knee volumes.

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