Diffusion Tensor Imaging and Beyond

By Jacques-Donald Tournier, Susumu Mori, Alexander Leemans. Magnetic Resonance in Medicine, 2011

953 citations recorded by the source corpus.

The diffusion of water molecules inside organic tissues is often anisotropic (1). Namely, if there are aligned structures in the tissue, the apparent diffusion coefficient (ADC) of water may vary depending on the orientation along which the diffusion-weighted (DW) measurements are taken. In the late 1980s, diffusion-weighted imaging (DWI) became possible by combining MR diffusion measurements with imaging, enabling the mapping of both diffusion constants and diffusion anisotropy inside the brain and revealing valuable information about axonal architectures (2-14). In the beginning of the 1990s, the diffusion tensor model was introduced to describe the degree of anisotropy and the structural orientation information quantitatively (15,16). This diffusion tensor imaging (DTI) approach provided a simple and elegant way to model this complex neuroanatomical information using only six parameters. Since then, we have witnessed a tremendous amount of growth in this research field, including more sophisticated nontensor models to describe diffusion properties and to extract finer anatomical information from each voxel. Three-dimensional (3D) reconstruction technologies for white matter tracts are also developing beyond the initial deterministic line-propagation models (17-20). As these new reconstruction methods are an area of very active research, it is important to remember that the theory cannot be dissociated from practical aspects of the technology. Importantly, DWI is inherently a noise-sensitive and artifact-prone technique (Fig. 1). Thus, we cannot overemphasize the importance of image quality assurance and robust image analysis techniques. Last but not least, data acquisition technologies have also been steadfastly evolving. In this article, we review the recent advances in these areas since 2000. FIG. 1 Examples of typical artifacts: (i) signal/slice dropouts, (ii) eddy-current induced geometric distortions, (iii) systematic vibration artifacts, and (iv) ghosting (insufficient/incorrect fat-suppression).

Subfields: Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/43ba725c7e1534dda2af382c83d6f9d62571d214

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Citations in the corpus

Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data (2014, positive): “However, this assumption is common to all non-parametric SD approaches (Kaden et al., 2007), as it leads to much better conditioned reconstructions, and is generally considered appropriate (Tournier et al., 2011).”
SIFT: Spherical-deconvolution informed filtering of tractograms (2013, methodology): “By encoding a series of images according to thediffusion ofwater in a number of non-collinear directions, and assessing the data using appropriate mathematical models, properties of the underlying structure (such as the orientations of axonal bundles) can be estimated (Tournier et al., 2011).”
Characterization of Cerebral White Matter Properties Using Quantitative MRI Stains (2011, background): “…such as the B0 magnetic field strength, gradient performance and coil sensitivity, and artifacts including pulsatile (e.g., cardiac) signal fluctuations , ghosting, and distortions from B0 field inhomogeneities and eddy currents; see Alexander et al. (2007) and Tournier et al. (2011) for reviews.”
Tractography - where do we go from here? (2011, neutral): “Data acquisition strategies ultimately depend on the subsequent modeling approach (Tournier et al., 2011) (and of course on the available hardware and scanning time).”
Investigating white matter fibre density and morphology using fixel-based analysis (2017, background): “In recent years, a number of more advanced diffusion MRI models have been proposed that can resolve multiple fibre populations in a single voxel (Tournier et al., 2011).”
Anatomically-constrained tractography: Improved diffusion MRI streamlines tractography through effective use of anatomical information (2012, background): “Within the brain white matter, characterisation of the anisotropic nature of the signal (achieved through acquisition of multiple images with varying diffusion sensitisation directions) allows for the estimation of the orientations of the underlying coherent neuronal axon populations (Tournier et al”
Methodological considerations on tract-based spatial statistics (TBSS) (2014, background): “Diffusion magnetic resonance imaging (MRI) can provide an insight into the living human brain in health and disease, especially in white matter anatomy, and provides quantitative parameters related to white matter (WM) microstructure (Tournier et al., 2011).”
Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging (2013, background): “, 2002], it is now possible to reliably extract fiber orientations in regions of increased complexity [Alexander, 2006; Tournier et al., 2011].”
MRtrix: Diffusion tractography in crossing fiber regions (2012, background): “It should be noted however that ambiguities related to ‘‘kissing’’ versus crossing ﬁber conﬁgurations are still unresolved, and remain problematic for diffusion tractography in general (Tournier et al., 2011).”
Diffusion MRI fiber tractography of the brain (2019, background): “Over the last decade, a range of so‐called “higher‐order” fiber modeling methods have been proposed, with the capability of estimating the orientations and relative contributions of multiple fiber populations within each voxel, without any explicit assumption regarding the number of underlying fiber”
White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI (2013, background): “motion, slice-drop outs, pulsatility and other perturbations (Jones and Leemans, 2011; Tournier et al., 2011).”