Deep learning-based Reconstruction with Super Resolution for Abdominal Diffusion Weighted Imaging

Motivation: Abdominal diffusion-weighted imaging (DWI) plays a significant role in the detection and characterization of lesions. However, the spatial resolution of single-shot echo-planar imaging (ssh-EPI) readout is limited by the acquisition time. Goal(s): To enhance the image quality and sharpness of abdominal ssh-EPI-DWI image using a prototype AI-based reconstruction technique (SuperRes). Approach: We examined eight healthy volunteers using abdominal ssh-EPI-DWI, and the acquired data were reconstructed using both conventional Compressed SENSE and SuperRes. The image quality was assessed qualitatively and quantitatively. Results: SuperRes demonstrated a significant improvement in the image quality and sharpness of both DWI and ADC map. Impact: The dedicated deep learning-based super-resolution technique enhanced the image quality and sharpness in abdominal ssh-EPI-DWI. Enhanced sharpness resulted in better delineation of structures, such as the pancreas. The improvement in image quality was demonstrated in both qualitative and quantitative assessments.