Post-contrast multi-parametric mapping from only pre-contrast conventional weighted images

Motivation: Gadolinium-based contrast agents (GBCAs) have become a cornerstone in clinical routine for lesions characterization and treatment monitoring. However, issues such as safety concerns related to deposition of GBCA in the body and brain, prolonged acquisitions, and cost increase advocate against its usage. Goal(s): To replace the usage of GBCAs in post-contrast imaging with parametric maps and deep learning. Approach: A cascade of two convolutional-neural-networks for pre- and post-contrast parametric mapping and the synthesis of post-contrast T1-weighted images from only two pre-contrast conventional weighted images. Results: The proposed approach presents potential for predicting post-contrast T1w-enhancement without the usage of GBCAs. Impact: The proposed deep learning approach provides both pre- and post-contrast parametric maps and, consequently, the capability of synthesizing any post-contrast image from only two pre-contrast conventional weighted images. Thus, it paves the way towards GBCAs-free acquisitions.