Twenty‐five pitfalls in the analysis of diffusion MRI data

Abstract

Obtaining reliable data and drawing meaningful and robust inferences from diffusion MRI can be challenging and is subject to many pitfalls. The process of quantifying diffusion indices and eventually comparing them between groups of subjects and/or correlating them with other parameters starts at the acquisition of the raw data, followed by a long pipeline of image processing steps. Each one of these steps is susceptible to sources of bias, which may not only limit the accuracy and precision, but can lead to substantial errors. This article provides a detailed review of the steps along the analysis pipeline and their associated pitfalls. These are grouped into 1 pre-processing of data; 2 estimation of the tensor; 3 derivation of voxelwise quantitative parameters; 4 strategies for extracting quantitative parameters; and finally 5 intra-subject and inter-subject comparison, including region of interest, histogram, tract-specific and voxel-based analyses. The article covers important aspects of diffusion MRI analysis, such as motion correction, susceptibility and eddy current distortion correction, model fitting, region of interest placement, histogram and voxel-based analysis. We have assembled 25 pitfalls (several previously unreported) into a single article, which should serve as a useful reference for those embarking on new diffusion MRI-based studies, and as a check for those who may already be running studies but may have overlooked some important confounds. While some of these problems are well known to diffusion experts, they might not be to other researchers wishing to undertake a clinical study based on diffusion MRI.

Comparison of Preoperative Quantitative Magnetic Resonance Imaging and Clinical Assessment of Deep Pain Perception as Prognostic Tools for Early Recovery of Motor Function in Paraplegic Dogs with Intervertebral Disk Herniations

“…Attempts to independently measure DTI metrics from white and gray matter using clinically applicable protocols could lead to partial volume effects;55 therefore, ROIs were placed in both, gray and white matter, and were positioned equally in all patients. Albeit no intramedullary signal voidance was noticed in T2* sequences, foci of intramedullary hemorrhage may have an impact in diffusion metrics 56…”

Identifying preoperative language tracts and predicting postoperative functional recovery using HARDI q-ball fiber tractography in patients with gliomas

“…The results were carefully inspected to exclude the possibility that susceptibility distortions may have affected the outcome of the tracking. 17 In vivo diffusion MRI fiber tractography was performed by a trained neuroradiologist pre-and postsurgery to reconstruct white matter bundles belonging to dorsal phonological pathways (arcuate fasciculus [AF] We describe below the tracking approach for each white matter pathway studied.…”

Diffusion magnetic resonance imaging for Brainnetome: A critical review

“…There are major challenges for diffusion modeling and tractography [134,135] . First, DWI signals are very noisy, especially for signals with high b-values.…”