Improving Image Quality of Dynamic Contrast Enhanced Abdominal MRI Using a Novel Deep Learning Reconstruction

Abstract

Motivation: Free breathing DCE imaging utilizes stack-of-stars sampling, which can lead to streak artifacts and noise reduction when too few spokes are used. Goal(s): Our goal was to validate application of deep learning to 3D DISCO-Star DCE imaging in the abdomen via image quality assessment and noise characterization. Approach: DL and conventionally reconstructed images were assessed by two radiologists across different IQ attributes. Noise characteristics were evaluated by calculation of total variation. AUC was also calculated. Results: The radiologists preferred DL across many of the IQ attributes, with noticeably lowered noise and decreased streaks in DL images. AUC was similar between the two reconstructions. Impact: The application of DL to DISCO-Star DCE imaging provides enhanced diagnostic quality, with reduced streaking, higher SNR, and better in-plane resolution. This has the potential to improve care for abdominal patients who have trouble holding their breath.