Abstract #0390

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

Ziyu Li¹, Qiyuan Tian², Chanon Ngamsombat^2,3, Samuel Cartmell⁴, John Conklin^2,4, Augusto Lio M. Gonçalves Filho^2,4, Wei-Ching Lo⁵, Guangzhi Wang¹, Kui Ying⁶, Kawin Setsompop^7,8, Qiuyun Fan², Berkin Bilgic², Stephen Cauley², and Susie Y Huang^2,4

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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