J
Jiahao Huang
Researcher at National Institutes of Health
Publications - 5
Citations - 58
Jiahao Huang is an academic researcher from National Institutes of Health. The author has contributed to research in topics: Deep learning & Iterative reconstruction. The author has an hindex of 1, co-authored 5 publications receiving 2 citations. Previous affiliations of Jiahao Huang include Beijing Institute of Technology.
Papers
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Transfer learning enhanced generative adversarial networks for multi-channel MRI reconstruction
TL;DR: In this paper, a pre-trained generative adversarial network (GAN) and transfer learning was used to improve the reconstruction performance of a small number of training samples for real clinical applications.
Journal ArticleDOI
FA-GAN: Fused attentive generative adversarial networks for MRI image super-resolution
Mingfeng Jiang,Minghao Zhi,Liying Wei,Xiaocheng Yang,Jucheng Zhang,Yongming Li,Pin Wang,Jiahao Huang,Guang Yang +8 more
TL;DR: Li et al. as discussed by the authors proposed a framework called the Fused Attentive Generative Adversarial Networks(FA-GAN) to generate the super-resolution MR image from low-resolution magnetic resonance images, which can reduce the scanning time effectively but with high resolution MR images.
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FA-GAN: Fused Attentive Generative Adversarial Networks for MRI Image Super-Resolution.
Mingfeng Jiang,Minghao Zhi,Liying Wei,Xiaocheng Yang,Jucheng Zhang,Yongming Li,Pin Wang,Jiahao Huang,Guang Yang +8 more
TL;DR: Li et al. as mentioned in this paper proposed a framework called the Fused Attentive Generative Adversarial Networks(FA-GAN) to generate the super-resolution MR image from low-resolution magnetic resonance images, which can reduce the scanning time effectively but with high resolution MR images.
Posted Content
Transfer Learning Enhanced Generative Adversarial Networks for Multi-Channel MRI Reconstruction
TL;DR: In this paper, a pre-trained generative adversarial network (GAN) and transfer learning was used to improve the reconstruction performance of a pretrained GAN model with parallel imaging.
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Generative Adversarial Networks (GAN) Powered Fast Magnetic Resonance Imaging - Mini Review, Comparison and Perspectives.
TL;DR: Recently, Generative Adversarial Networks (GAN) based methods have been proposed to solve fast MRI with enhanced image perceptual quality as discussed by the authors, where the encoder obtains a latent space for the undersampled image, and the image is reconstructed by the decoder using the GAN loss.