Twenty‐five pitfalls in the analysis of diffusion MRI data

By Derek K. Jones, Mara Cercignani. NMR in Biomedicine, 2010

910 citations recorded by the source corpus.

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. Copyright © 2010 John Wiley & Sons, Ltd.

Subfields: Pediatrics, Perinatology and Child Health, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/77b79e9017e24f202f71fe2394bc43def105c428

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Pediatrics, Perinatology and Child Health

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Q-space trajectory imaging for multidimensional diffusion MRI of the human brain (2016, neutral): “The fit was corrected for heteroscedasticity using HS=HXβ as the regression model instead of S=Xβ, where H is a diagonal matrix with the signal amplitudes as diagonal elements (Jones and Cercignani, 2010).”
The cingulum bundle: Anatomy, function, and dysfunction (2018, neutral): “For this reason, it is difficult to interpret differences/changes in FA and diffusivity metrics in terms of any specific biophysical property of white matter (Wheeler-Kingshott and Cercignani, 2009; Jones, 2010).”
White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI (2013, background): “There are, however, a substantial number of pitfalls associatedwith thesemethods (see, e.g., Jones, 2010b, 2010c; Jones and Cercignani, 2010; Le Bihan et al., 2006), which can lead to biased or, in some cases, completely fallacious conclusions being drawn.”
Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging (2013, methodology): “In r 2764 r addition, low-FA values can become unreliable in the presence of noise [Jones and Cercignani, 2010].”
Great Expectations: Using Whole-Brain Computational Connectomics for Understanding Neuropsychiatric Disorders (2014, background): “Yet, there are also significant limitations to these methods including the indirect nature of connectivity measures and the lack of information of directionality (Jones and Cercignani, 2010).”
Spurious group differences due to head motion in a diffusion MRI study (2013, background): “Anisotropy and diffusivity measures derived from DW-MRI are sensitive to several confounding factors, including head motion, partial volume, and fiber crossing effects (Jones and Cercignani, 2010;MetzlerBaddeley et al., 2012; Jones et al., 2013).”
The hubs of the human connectome are generally implicated in the anatomy of brain disorders (2014, background): “Although DTI provides the most direct access to the structural network of the living human brain, it also has limitations (Morris et al., 2008; Jones and Cercignani, 2010; Craddock et al., 2013; Jones et al., 2013).”
PANDA: a pipeline toolbox for analyzing brain diffusion images (2013, background): “These issues have been well discussed by a few recent articles (Jones and Cercignani, 2010; Jones et al., 2012).”
A hitchhiker's guide to diffusion tensor imaging (2012, methodology): “…with diffusion MRI properties such as eddy current-induced distortions and general MRI issues like sensitivity to motion and B0-susceptibility artifacts (Farzaneh et al., 1990; Basser and Jones, 2002; Le Bihan et al., 2006; Kaur et al., 2007; Bammer et al., 2009; Jones and Cercignani, 2010).”
How and how not to correct for CSF-contamination in diffusion MRI (2012, neutral): “This is true, for example, for the fornix and the genu of corpus callosum (Concha et al., 2005; Jones and Cercignani, 2010).”
Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data (2014, neutral): “Correction included the required B-matrix adjustments (Leemans and Jones, 2009) and appropriate modulation of the DW images with the Jacobian of the transformation matrix (Jones and Cercignani, 2010).”
The Angular Gyrus (2013, background): “It is, however, important to keep in mind some of the limitations in using such techniques to quantify structural connectivity in vivo (see review in Jones 2010; Jones and Cercignani 2010).”
MRtrix: Diffusion tractography in crossing fiber regions (2012, background): “In practice, it may be necessary to correct for such artifacts (Jones and Cercignani, 2010), particularly when acquiring data on noncompliant subjects, or when using acquisition sequences that are more sensitive to eddy-currents, in particular single spin-echo sequences.”