Deep Learning-based Distortion Correction of Diffusion-weighted Imaging

Abstract

Motivation: Diffusion-weighted imaging (DWI) is typically based on single-shot echo-planar imaging (EPI), which is prone to magnetic field inhomogeneities-induced artifacts, such as geometric distortion and blurring. The multi-shot diffusion sequence, DIADEM, employing a dual spin-warp (SW) and EPI phase-encoding strategies, can produce distortion-free images at the cost of extended scan times. Goal(s): We proposed a deep learning-based distortion correction method for conventional DWI, using DIADEM as reference. Approach: The 3D neural network was trained to learn the mapping between the projections of the point-spread-function, PSF H(y,s) along the EPI phase-encoding (y) direction and the PSF-encoding (s) direction, respectively. Results: It demonstrated reduced geometric distortion. Impact: Conventional DWI sequence suffers from distortion caused by susceptibility. We proposed a deep learning-based distortion correction method, leveraging distortion-free DIADEM images as reference. Our method was demonstrated to reduce geometric distortion and imaging blurring without distortion calibration.