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Abstract #3940

Reconstruction of highly accelerated free-breathing 3D abdominal MRI using Stacked Convolutional Auto-Encoder Network

Jun Lv1, Kun Chen1, Ming Yang2, Jue Zhang1,3, Xiaoying Wang1,4, and Jing Fang1,3

1Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, People's Republic of China, 2Philips Healthcare, Suzhou, People's Republic of China, 3College of Engineering, Peking University, Beijing, People's Republic of China, 4Department of Radiology, Peking University First Hospital, Beijing, People's Republic of China

Free-breathing 3D abdominal imaging is a challenging task for MRI since respiratory motion severely degrades image quality.Our purpose is to develop a novel reconstruction approach for highly accelerated free-breathing 3D abdominal images with stacked convolutional auto-encoders. The whole structure of our proposed method consists of 9 hidden layers except to input and output layer.The proposed method achieves similar quality to the whole sampling reconstruction with non-significant differences for structural similarity index measure (SSIM) (0.99 and 1.00, respectively). Moreover, the average reconstruction time is very short (about 0.25 s/image). Therefore, our method should be employed for a wide range of clinical applications.

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