Deep learning-based image quality and spatial resolution improvement for Diffusion Weighted Imaging in liver

Diffusion-weighted imaging (DWI) in liver plays a significant role for lesion characterization and staging of fibrosis. Single-shot echo-planar imaging (ssh-EPI) readout is typically used; however, spatial resolution of ssh-EPI-DWI is limited by acquisition time. In this study, we investigated the use of prototype AI-based reconstruction technique (SmartSpeed Precise Image) to improve the image quality of liver ssh-EPI-DWI images. The image quality was compared between conventional Compressed-SENSE (C-SENSE), SmartSpeed AI, and SmartSpeed Precise Image. Volunteer data demonstrated a significant improvement of sharpness in DWI images and ADC map, and reduction of ringing artifact compared with C-SENSE and SmartSpeed AI reconstruction.