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Abstract #0390

HDnGAN: High-fidelity ultrafast volumetric brain MRI using a hybrid denoising generative adversarial network

Ziyu Li1, Qiyuan Tian2, Chanon Ngamsombat2,3, Samuel Cartmell4, John Conklin2,4, Augusto Lio M. Gonçalves Filho2,4, Wei-Ching Lo5, Guangzhi Wang1, Kui Ying6, Kawin Setsompop7,8, Qiuyun Fan2, Berkin Bilgic2, Stephen Cauley2, and Susie Y Huang2,4
1Department of Biomedical Engineering, Tsinghua University, Beijing, China, 2Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States, 3Department of Radiology, Siriraj Hospital, Mahidol University, Bangkok, Thailand, 4Department of Radiology, Massachusetts General Hospital, Boston, MA, United States, 5Siemens Medical Solutions, Boston, MA, United States, 6Department of Engineering Physics, Tsinghua University, Beijing, China, 7Department of Radiology, Stanford University, Stanford, CA, United States, 8Department of Electrical Engineering, Stanford University, Stanford, CA, United States

Highly accelerated high-resolution volumetric brain MRI is intrinsically noisy. A hybrid generative adversarial network (GAN) for denoising (entitled HDnGAN) consisting of a 3D generator and a 2D discriminator was proposed to improve the SNR of highly accelerated images while preserving realistic textures. The novel architecture benefits from improved image synthesis performance and increased training samples for training the discriminator. HDnGAN's efficacy is demonstrated on 3D standard and Wave-CAIPI T2-weighted FLAIR data acquired in 33 multiple sclerosis patients. Generated images are similar to standard FLAIR images and superior to Wave-CAIPI and BM4D-denoised images in quantitative evaluation and assessment by neuroradiologists.

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