Optimizing Temporal Resolution of Dynamic Contrast Enhanced Abdominal MRI Using Deep Learning Reconstruction

Abstract

Motivation: Free breathing DCE imaging is beneficial for patients who have difficulty holding their breath, but can be susceptible to artifacts and suboptimal contrast bolus timing, which may compromise diagnostic accuracy. Goal(s): Our goal was to validate application of deep learning to 3D DISCO-Star imaging in the abdomen after doubling the number of wash-in phases via spoke reordering. Approach: 8 and 16 wash-in phase images were assessed by two radiologists across different IQ attributes. Noise characteristics were evaluated and AUC was calculated. Results: The radiologists preferred DL enhanced 16 wash-in phase across many of the IQ attributes, with higher SNR and decreased streaks. Impact: The ability to double the wash-in phases in DISCO-Star DCE imaging without compromising image quality via deep learning will provide enhanced diagnostic quality, and has the potential to improve patient care.