Evaluating corrections for Eddy‐currents and other EPI distortions in diffusion MRI: methodology and a dataset for benchmarking. Issue 4 (5th November 2018)

• Record Type: Journal Article
• Authors: Irfanoglu, M. Okan; Sarlls, Joelle; Nayak, Amritha; Pierpaoli, Carlo
• Is Part Of: Magnetic resonance in medicine. Volume 81:Issue 4(2019)
• Page Start: 2774
• Page End: 2787
• Publication Date: 2018-11-05
• Subjects: diffusion MRI -- distortion correction -- eddy‐currents -- EPI; Nuclear magnetic resonance -- Periodicals; Electron paramagnetic resonance -- Periodicals; 616.07548
• DOI: 10.1002/mrm.27577
• Languages: English
• ISSNs: 0740-3194

Abstract

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.