Keywords: Machine Learning/Artificial Intelligence, Machine Learning/Artificial Intelligence, Super Resolution
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.
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