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.