Acquiring perfusion maps from contrast-enhanced MRA using deep learning approaches

Abstract

Perfusion maps and dynamic angiograms are both important for stroke/tumor treatment but commonly acquired in separate scans and thus may require additional injection of contrast agent for best results. In this work, we present a deep learning method to acquire perfusion maps from contrast-enhanced MRA data. Our results showed that an architecture of multiple decoders and an enhanced encoder produced perfusion maps that were visually and quantitatively similar to the standard DSC MRI-based perfusion maps. This approach enables us to acquire accurate perfusion maps and angiogram using a single contrast agent injection, reducing costs and risks while improving patient comfort.