Exogenous Agent-Free Synthetic Post-contrast Imaging with a Cascade of Deep Networks for Enhancement Prediction After Tumor Resection

Abstract

Gadolinium-based contrast agents (GBCAs) have become a cornerstone in clinical routine for detection, characterization and monitoring of several diseases. Particularly, GBCAs are clinically relevant for the detection of blood brain barrier (BBB) damage, which is associated with an aggressive tumor behavior. However, issues such as safety concerns related to deposition of GBCA in the brain, prolonged acquisitions, and cost increase advocate against its usage. In this work, we propose a novel approach based on a cascade of deep networks for pre- and post-contrast parametric mapping and the synthesis of post-contrast T1-weighted images. Only a pair of pre-contrast weighted images acquired with conventional pulse sequences are used as inputs; thus, our approach is GBCAs-free. Results reveal the potential of this approach to obtain T1w-enhancement information after tumor resection which is comparable with another state-of-the-art prediction approach. We provide not only the predictions, but also the pre- and post-contrast parametric maps without the usage of GBCAs.