Evaluating corrections for Eddy-currents and other EPI distortions in diffusion MRI: methodology and a dataset for benchmarking

Magn Reson Med. 2019 Apr;81(4):2774-2787. doi: 10.1002/mrm.27577. Epub 2018 Nov 5.

Abstract

Purpose: To propose a methodology for assessment of algorithms that correct distortions due to motion, eddy-currents, and echo planar imaging in diffusion weighted images (DWIs).

Methods: The proposed method evaluates correction performance by measuring variability across datasets of the same object acquired with images having distortions in different directions, thereby overcoming the unavailability of ground-truth, undistorted DWIs. A comprehensive diffusion MRI dataset, collected using a suitable experimental design, is made available to the scientific community, consisting of three DWI shells (Bmax = 5000 s/mm2 ), 30 gradient directions, a replicate set of antipodal gradient directions, four phase-encoding directions, and three different head orientations. The proposed methodology was tested using the TORTOISE diffusion MRI processing pipeline.

Results: The median variability of the original distorted data was 123% higher for DWIs, 100-168% higher for tensor-derived metrics and 28-111% higher for MAPMRI metrics, than in the corrected versions. EPI distortions induced substantial variability, nearly comparable to the contribution of eddy-current distortions.

Conclusions: The dataset and the evaluation strategy proposed herein enable quantitative comparison of different methods for correction of distortions due to motion, eddy-currents, and other EPI distortions, and can be useful in benchmarking newly developed algorithms.

Keywords: EPI; diffusion MRI; distortion correction; eddy-currents.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Algorithms
  • Anisotropy
  • Artifacts
  • Brain / diagnostic imaging*
  • Databases, Factual
  • Diffusion Magnetic Resonance Imaging*
  • Echo-Planar Imaging*
  • Head
  • Humans
  • Image Enhancement / methods
  • Image Interpretation, Computer-Assisted / methods
  • Image Processing, Computer-Assisted / methods
  • Motion
  • Probability
  • Reproducibility of Results