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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