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/mm(2) ), 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.