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Abstract #1789

Predicting Perfusion Augmentation Using Deep Learning without Vasodilators

Moss Zhao1, Ramy Hussein1, Michael Moseley1, and Greg Zaharchuk1
1Department of Radiology, Stanford University, Stanford, CA, United States


We present a deep learning technique to predict cerebral perfusion after vasodilation challenges. A 3D convolutional neural network (CNN)-based encoder-decoder architecture was constructed to transform ASL perfusion images acquired pre-vasodilation into post-vasodilation images using an improved attention-gated 3D U-Net. Results showed that the prediction and ground truth were not significantly different. This technique will enable a drug-free MR procedure to study the hemodynamic of patients with high risk cerebrovascular diseases.

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