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Abstract #4706

Contrast Transfer Learning for Reconstruction of Undersampled Dynamic Contrast-Enhanced MRI

Li Feng1, Fang Liu2, Lihua Chen3,4, and Ricardo Otazo1,5

1Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States, 2Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States, 3Department of Radiology, Southwest Hospital, Chongqing, China, 4Department of Radiology, PLA 101st Hospital, Wuxi, Jiangsu, China, 5Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States

The application of deep learning for reconstruction of dynamic contrast-enhanced MRI presents significant challenges caused by the rapid passage of the contrast agent, which makes it difficult to acquire fully-sampled images to train a neural network. This work proposes to use images from a delayed contrast phase, where contrast changes are in a relatively steady state, for training, and to apply the trained neural network for reconstruction of undersampled data acquired in other contrast phases. The proposed contrast transfer learning reconstruction was trained on 55 post-contrast liver cases and tested on a first-pass liver DCE-MR acquisition.

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