Abstract #4055

MRI image synthesis with a conditional generative adversarial network using patch pooling

Bragi Sveinsson^1,2 and Matthew S Rosen^1,2,3

¹Martinos Center, Massachusetts General Hospital, Boston, MA, United States, ²Harvard Medical School, Boston, MA, United States, ³Physics, Harvard University, Cambridge, MA, United States

Deep learning networks allow the creation of new images based on a separate set of reference image data. This can be used to synthesize a specific MRI contrast from other image contrasts sharing the same anatomy. A particularly successful approach uses a conditional generative adversarial network with a patch-based discriminator, processing image patches of a fixed size. In this work, we investigate the benefits of using multiple patch sizes to improve image quality.

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