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.

This article presents the potential problems arising from the use of "axial" and "radial" diffusivities, derived from the eigenvalues of the diffusion tensor, and their interpretation in terms of the underlying biophysical properties, such as myelin and axonal density. Simulated and in vivo data are shown. The simulations demonstrate that a change in "radial" diffusivity can cause a fictitious change in "axial" diffusivity and vice versa in voxels characterized by crossing fibers. The in vivo data compare the direction of the principle eigenvector in four different subjects, two healthy and two affected by multiple sclerosis, and show that the angle, ␣, between the principal eigenvectors of corresponding voxels of registered datasets is greater than 45°in areas of low anisotropy, severe pathology, and partial volume. Also, there are areas of white matter pathology where the "radial" diffusivity is 10% greater than that of the corresponding normal tissue and where the direction of the principal eigenvector is altered by more than 45°compared to the healthy case. This should strongly discourage researchers from interpreting changes of the "axial" and "radial" diffusivities on the basis of the underlying tissue structure, unless accompanied by a thorough investigation of their mathematical and geometrical properties in each dataset studied. Since the early publications by Basser et al. (1,2), diffusion tensor imaging (DTI) has evolved and expanded noticeably its application to clinical studies moving toward modeling the tissue microstructure (3-5) and reconstructing white matter tracts (6 -8).While the elements of the tensor matrix are different for each system of coordinates, the DT can be diagonalized to extract its three eigenvalues, 1 , 2 , and 3 , which can be combined to define quantitative parameters such as mean diffusivity (MD) and fractional anisotropy (FA), which are rotationally invariant and independent of eigenvalue sorting.Since Song et al. (9) published their article where they look at the "axial diffusivity," i.e., the principal eigenvalue of the DT, and at the "radial diffusivity," i.e., the average of the second and third eigenvalues of the DT, in an animal model, and where they link the radial diffusivity with myelin content, studies reporting comparisons of these indices are becoming very frequent (e.g., 10 -14). It is important to underline the fact that the direction of the principal eigenvector with eigenvalue 1 , i.e., the direction of the "axial" diffusivity, is not always preserved in pathological tissue and is not always aligned with the underlying expected tissue architecture (15).It has been thoroughly shown (e.g., 28) that the direction and the magnitude of the eigenvalues and eigenvectors are physical measures that are affected by the noise, the shape of the calculated diffusion ellipsoid, and pathology. With this study we do not claim to propose a new method for interpreting DTI data or for solving the problem of the sorting bias already extensively investigated (16 -18). Here we wo...

Objective: To investigate the extent and the nature of white matter tissue damage of patients with Alzheimer's disease using diffusion tensor magnetic resonance imaging (DT-MRI). Background: Although Alzheimer's disease pathology mainly affects cortical grey matter, previous pathological and MRI studies showed that also the brain white matter of patients is damaged. However, the nature of Alzheimer's disease associated white matter damage is still unclear. Methods: Conventional and DT-MRI scans were obtained from16 patients with Alzheimer's disease and 10 sex and age matched healthy volunteers. The mean diffusivity (D), fractional anisotropy (FA), and inter-voxel coherence (C) of several white matter regions were measured. Results: D was higher and FA lower in the corpus callosum, as well as in the white matter of the frontal, temporal, and parietal lobes from patients with Alzheimer's disease than in the corresponding regions from healthy controls. D and FA of the white matter of the occipital lobe and internal capsule were not different between patients and controls. C values were also not different between patients and controls for any of the regions studied. Strong correlations were found between the mini mental state examination score and the average overall white matter D (r=0.92, p<0.001) and FA (r=0.78; p<0.001). Conclusions: White matter changes in patients with Alzheimer's disease are likely to be secondary to wallerian degeneration of fibre tracts due to neuronal loss in cortical associative areas.

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