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Abstract #4845

3D MRI Denoising with Wasserstein Generative Adversarial Network

Maosong Ran1, Jinrong Hu2, Yang Chen3, Hu Chen1, Huaiqiang Sun4, Jiliu Zhou1, and Yi Zhang1

1College of Computer Science, Sichuan University, Chengdu, China, 2Department of Computer Science, Chengdu University of Information Technology, Chengdu, China, 3Lab of Image Science and Technology, Southeast University, Nanjing, China, 4Department of Radiology, West China Hospital of Sichuan University, Chengdu, China

MR image is easily affected by noise during the high-speed and high-resolution acquisition procedure. To effectively remove the noise and fully explore the potential of latest technique -- deep learning, in this abstract, we propose a novel MRI denoising method based on generative adversarial network. Specifically, to explore the structure similarity among neighboring slices, 3-D configuration are utilized as the basic processing unit. Residual autoencoder, combined with deconvolution operations are introduced into the generator network. The experimental results show that the proposed method achieves superior performance relative to several state-of-art methods in both noise suppression and structure preservation.

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