Fig. 2. Examples showing the ability of deep learning to generate realistic fake images. (a) Representative test images from the trained network for generating.

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Fig. 2. Examples showing the ability of deep learning to generate realistic fake images. (a) Representative test images from the trained network for generating either pizza images from T1-weighted MR images or T1-weighted MR images from pizza images. (b) Representative test images from the tr ained network for generating MR diffusion-weighted images from actual CT images. The network generated the realistic synthesized diffusion-weighted image from the actual CT image. However, this generated diffusion-weighted image does not include the pathologic information which appears on the corresponding actual diffusion-weighted image. These two examples were generated using the generative adversarial networks, which are popular deep neural networks used for image-to-image translation tasks (18, 19, 20). Fig. 2. Examples showing the ability of deep learning to generate realistic fake images. (a) Representative test images from the trained network for generating either pizza images from T1-weighted MR images or T1-weighted MR images from pizza images. (b) Representative test images from… Investig Magn Reson Imaging. 2019 Jun;23(2):81-99. https://doi.org/10.13104/imri.2019.23.2.81