Meeting Banner
Abstract #1586

An generalized single MR image super resolution approach using combined super-resolution network and cycle-consistent adversarial network

Botian Xu1,2, Yaqiong Chai1,2, Kangning Zhang3, Natasha Lepore1,2, and John Wood1,2

1Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, United States, 2Children's Hospital Los Angeles, Los Angeles, CA, United States, 3Department of Electrical and Computer Engineering, University of California, Davis, Davis, CA, United States

Traditional inception-based convolutional neural networks (CNN) are proved to be capable of tackling high resolution image restoration, yet they are poor at generalization due to the supervised learning procedure. We proposed a combination of CNN-based super resolution network and generative adversarial network, to make full use of the learning of high resolution from CNN, as well as to improve the generalization of the network, by preserving the original contrast of the sequence. The result shows that our proposed network could perform MR super resolution across sequences with higher quality than that from a single CNN network.

This abstract and the presentation materials are available to members only; a login is required.

Join Here