Application of a deep learning reconstruction to routine liver 3D LAVA-Flex acquisitions

Abstract

Motivation: Fat suppressed T1 images, such as LAVA-FLEX, are routinely used in liver imaging, but can suffer from SNR and IQ issues. Goal(s): Our goal was to validate application of 3D deep learning to 3D LAVA-FLEX in routine adult liver imaging via a reader study and noise characterization. Approach: DL and conventionally reconstructed images were assessed across several IQ attributes (motion, ringing, edge, vessel) by two radiologists. Noise characteristics were evaluated by calculation of total variation and edge detection. Results: Based on the calculated odds ratios, the radiologists preferred DL across the various IQ attributes, with decreased noise and improved sharpness in DL images. Impact: The application of 3D DL to routine 3D LAVA-FLEX imaging provides increased diagnostic quality, and has the potential to improve routine abdominal care in patients who can't hold their breath.