Sewon Kim1, Hanbyol Jang2, Seokjun Hong2, Yeong Sang Hong3, Won C. Bae4,5, Sungjun Kim*3,6, and Dosik Hwang*2
1Electrical and electronic engineering, Yonsei University, Seoul, Korea, Republic of, 2Yonsei University, Seoul, Korea, Republic of, 3Gangnam Severance Hospital, Seoul, Korea, Republic of, 4Department of Radiology, University of California-San Diego, San Diego, CA, United States, 5Department of Radiology, VA San Diego Healthcare System, San Diego, CA, United States, 6Yonsei University College of Medicine, Seoul, Korea, Republic of
1Electrical and electronic engineering, Yonsei University, Seoul, Korea, Republic of, 2Yonsei University, Seoul, Korea, Republic of, 3Gangnam Severance Hospital, Seoul, Korea, Republic of, 4Department of Radiology, University of California-San Diego, San Diego, CA, United States, 5Department of Radiology, VA San Diego Healthcare System, San Diego, CA, United States, 6Yonsei University College of Medicine, Seoul, Korea, Republic of
we proposed a Bloch
equation-based autoencoder regularization generative adversarial network
(BlochGAN) for MR image synthesis. BlochGAN uses multi-contrast MR images to
generate other contrast images without additional scanning.
Entire network structure of
the Bloch equation-based autoencoder regularization generative adversarial
network (BlochGAN).
Comparison between the
reference and the generated T2 fat saturation images from dataset 1. Rows 2, 4,
and 6 are the magnified images of the areas in rows 1, 3, and 5 indicated by
red arrows.