Keywords: MR Fingerprinting/Synthetic MR, Machine Learning/Artificial IntelligenceGadolinium based contrast agents (GBCAs) have the ability to uncover blood brain barrier damage, which appears in the images as contrast enhancement caused by the leakage into the perivascular tissues. However, in clinical practice, this assessment is performed by visual comparison between the weighted images obtained before and after the GBCA injection; enhancement quantification is still an unmet need. In this work we propose a deep learning approach for the computation of pre- and post-contrast parametric maps from conventional T1 weighted images. Results show how those maps can enable an automatic quantification of the tumor enhancement.
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