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Abstract #1091

Contrast-free MRI Contrast Enhancement with Deep Attention Generative Adversarial Network

Jiang Liu1, Enhao Gong2,3, Thomas Christen4, and Greg Zaharchuk2

1Tsinghua University, Beijing, China, 2Stanford University, Stanford, CA, United States, 3Subtle Medical Inc., Menlo Park, CA, United States, 4Grenoble Institute of Neurosciences, Grenoble, France

While gadolinium-based contrast agents (GBCAs) have been dispensable for magnetic resonance imaging in clinic practice, recently there are rising concerns about the safety of GBCAs. In previous studies, we tested the feasibility of predicting contrast agents from pre-contrast images via deep convolutional neural networks. In this study, we further improved the results with deep attention generative adversarial network. The image similarity metrics show that the model can synthesize post-contrast T1w images with superior image quality and great resemblance to the ground truth images. Restoration of contrast uptake is clearly noted on the synthesized images even in small vessels and lesions.

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