Invest Radiol 2021 Jul 28. Epub 2021 Jul 28.
From the Imaging Department, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif BioMaps (UMR1281), Université Paris-Saclay, CNRS, Inserm, CEA, Villejuif Guerbet Research, Villepinte Center for Visual Computing, CentraleSupélec, Inria, Université Paris-Saclay, Gif-sur-Yvette, France.
This study proposes and evaluates a deep learning method that predicts surrogate images for contrast-enhanced T1 from multiparametric magnetic resonance imaging (MRI) acquired using only a quarter of the standard 0.1 mmol/kg dose of gadolinium-based contrast agent. In particular, the predicted images are quantitatively evaluated in terms of lesion detection performance. Read More