T1 mapping of the entire abdomen using a time efficient free breathing neural network radial Look Locker approach

The interest in developing quantitative metrics in abdominal imaging has grown in recent years. In particular, abdominal T1 mapping plays a role in the characterization of abdominal pathologies. However, current T1 mapping of the abdomen is limited by poor anatomical coverage, long acquisitions related to sufficient sampling of the T1 recovery curve and recovery times, and reduced T1 accuracy secondary to respiratory motion. Here we present a novel approach for free-breathing T1 mapping of the abdomen, which leverages the undersampling robustness of radial MRI and combines fast data acquisition with deep learning for accurate and efficient abdominal T1 mapping.