Anomaly-aware multi-contrast deep learning model for reduced gadolinium dose in contrast-enhanced brain MRI - a feasibility study

Deep learning (DL) has recently been proven to be effective in addressing the safety concerns of Gadolinium-based Contrast Agents (GBCAs). Recent studies have shown that DL-based algorithms are able to reconstruct contrast-enhanced MRI images with only a fraction of the standard dose. This work investigates the feasibility of improving the performance of such DL algorithms using multi-contrast MRI data, combined with an unsupervised anomaly detection based attention mechanism.