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Abstract #0810

Generative Adversarial Network for T2-Weighted Fat Saturation MR Image Synthesis Using Bloch Equation-based Autoencoder Regularization

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

We proposed a Bloch equation-based autoencoder regularization Generative Adversarial Network (BlochGAN) to generate T2-weighted fat saturation (T2 FS) images from T1-weighted (T1-w) and T2-weighted (T2-w) images for spine diagnosis. Our method can reduce the cost for acquiring multi-contrast images by reducing the number of contrasts to be scanned. BlochGAN properly generates the target contrast images by using GAN trained with the autoencoder regularization based on bloch equation, which is the basic principal of MR physics for identifying the physical basis of the contrasts. Our results demonstrate that BlochGAN achieved quantitatively and qualitatively superior performance compared to conventional methods.

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