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Abstract #4595

Reconstruction of Undersampled Radial Free-breathing 3D Abdominal MRI using Conditional Generative Adversarial Network

Jun Lv1 and Jue Zhang2,3

1School of Computer and Control Engineering, Yantai University, Yantai, China, 2b. Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China, 3c. College of Engineering, Peking University, Beijing, China

Free-breathing 3D abdominal imaging is challenging since respiratory motion can produce image blurring and ghosting artifact. Our purpose is to employ a novel deep learning method using conditional generative adversarial network (GAN) to reconstruct the undersampled radial 3D abdominal MRI. The whole network combines a generator G consists of 8 convolutional layers and corresponding 8 deconvolutional layers with a discriminator D which is formed using 11 convolutional layers. The GAN-based reconstructed images achieve similar quality to the ground-truth images. Additionally, the average reconstruction time is negligible. Therefore, this method can be adopted for a wide range of clinical applications.

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