Deep learning has found wide application in medical image reconstruction, transformation, and analysis tasks. Unlike typical machine learning workflows, MRI researchers are able to change the characteristics of images that are used as inputs to deep learning models. We proposed an algorithm that allows us to visualize the “ideal” input images that would provide the least error for a trained deep neural network. We apply this visualization technique on a deep convolutional neural network that converts Dixon MRI to synthetic CT images. We briefly characterize the optimization behavior and qualitatively analyze the features of the “ideal” input image.