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.