Keywords: Machine Learning/Artificial Intelligence, Diffusion/other diffusion imaging techniques, Diffusion acquisition, distortion reduction, deep learning
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.
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