High-Resolution Deep Learning Reconstruction (HR-DLR) to Improve Sharpness in Diffusion Weighted Imaging

Motivation: Echo planar diffusion weighted imaging (EPI-DWI) often suffers from Gibbs ringing artifact and/or image blurring, because of limited matrix size. A recently proposed High-Resolution Deep Learning Reconstruction (HR-DLR) may bring a breakthrough to the limitation. Goal(s): Our goal was to test benefits of HR-DLR when applied to brain EPI-DWI. Approach: HR-DLR was compared to conventional reconstruction method (zero-filling interpolation[ZIP] and low-pass filtering) with regards to image sharpness and ringing artifact suppression, with a conventional and an accelerated scan conditions. Results: The advantage of HR-DLR over the conventional method was confirmed by measurements of edge slope width (ESW) and ringing variable mean (RVM). Impact: A recently proposed High-Resolution Deep Learning Reconstruction successfully improved the sharpness of single shot EPI-DWI while suppressing Gibbs artifacts. The method could help improve clinical confidence by increasing image resolution and gain examination throughput by shortening acquisition time.