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Abstract #3493

Predicting Contrast Agent Enhancement with Deep Convolution Networks

Thomas Christen1, Enhao Gong1, Jia Guo1, Michael M. Moseley1, and Greg Zaharchuk1

1Radiology, Stanford University, Stanford, CA, United States

In this study, we tested whether deep convolutional neural networks (CNNs) could predict what an image would look like if a contrast agent was injected in the body. We trained a network to use information contained in a non-contrast MR brain exam and create a synthetic T1w image acquired after gadolinium injection. Multiple datasets including patients with tumors were used for training. Great similarities were found between the predicted and the actual images acquired after contrast agent injection. If further validated, this approach could have great clinical utility in patients who cannot receive contrast.

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