Diffusion tensor imaging (DTI)
DTI in QSIRecon
DTI reconstruction is supported in QSIRecon using the
DSI Studio package (see init_dsi_studio_recon_wf())
or the TORTOISE package (see init_tortoise_estimator_wf()).
The DIPY approach is accessible in a reconstruction specification by using a node with
action: DTI_reconstruction and software: Dipy.
Also see qsirecon.interfaces.dipy.TensorReconstruction (primary DTI node),
The TORTOISE approach is accessible in a reconstruction specification by using a node with
action: estimate, software: TORTOISE, and the parameters: estimate_tensor subdictionary.
Also see qsirecon.interfaces.tortoise.EstimateTensor.
DTI Foundational Papers
DTI Concept Introduction: Basser et al. introduced DTI as a new modeling framework that computes a Gaussian diffusion tensor per voxel, yielding eigenvalues and eigenvectors that describe 3D water diffusion (Basser et al., 1994). This enabled mapping of water diffusion orientations in tissue and orientation-independent scalar measures like trace (sum of eigenvalues, related to Mean Diffusivity, MD). It established the idea that tensors encode more microstructural information than single-direction diffusivities.
Anisotropy quantification: Pierpaoli and Basser defined fractional anisotropy (FA), a normalized 0-1 index quantifying how anisotropic (directionally dependent) diffusion is (Pierpaoli and Basser, 1996). They demonstrated that earlier methods (diffusion measured in just a few directions) underestimated anisotropy, and introduced rotationally invariant metrics (FA, relative anisotropy, etc.). This work also cautioned that noise can bias anisotropy measures requiring eigenvalue ordering.
First Human DTI Maps: Pierpaoli et al. acquired the first DTI scans of the human brain, mapping principal diffusion directions and magnitudes (Pierpaoli et al., 1996). They observed that water diffuses ~3 times faster along axonal fibers than perpendicular to them in highly coherent tracts (e.g. corpus callosum). They also introduced Trace (D = 3×MD) as an orientation-invariant measure of overall diffusivity, which was roughly uniform in normal brain except higher in cortical gray matter due to its higher water content.
Eigenvalue-Derived Metrics: By the early 2000s, researchers began interpreting individual tensor eigenvalues. Song et al. first showed that axial diffusivity (AD, diffusion along the primary eigenvector, λ1) and radial diffusivity (RD, diffusion perpendicular to axons, mean of λ2 & λ3) can provide pathologically specific insights (Song et al., 2002). In a mouse model of demyelination, RD increased with myelin loss while AD stayed constant (since axons remained intact). This seminal finding established that increases in RD selectively indicate myelin degeneration, whereas decreases in AD are more tied to axonal injury - a distinction that has since informed numerous neuroimaging studies of white matter diseases.
DTI Studies Across The Lifespan
The tensor model has been used extensively in the human brain (Pierpaoli et al., 1996) including developmental neuroscience.
Neonatal Diffusivity: Neil et al. reported neonatal mean diffusivity values 1.5-2 times higher than in adults, with very low white-matter anisotropy (Neil et al., 1998). This reflects abundant free water and unmyelinated fibers at birth. Diffusivity drops and anisotropy rises steeply in the first postnatal months as the brain matures. Water compartmentalizes and myelination progresses (Ouyang et al., 2019).
Childhood to Adolescence: White matter development continues through childhood and the teen years. Longitudinal data showed steady FA increases and MD decreases in virtually all major tracts from age 5 into the 20s (Lebel and Beaulieu, 2011). Not all tracts mature simultaneously: early-developing motor/sensory pathways (e.g., internal capsule) reach adult-like FA by late adolescence, whereas association tracts in frontal and temporal lobes keep increasing in FA (and decreasing in RD) into the third decade. This prolonged maturation of frontal circuitry aligns with functional development of executive and cognitive abilities in late adolescence.
Whole Lifespan Trajectories: Cross-sectional analyses across the lifespan find that FA follows an “inverted U” trajectory: increasing from childhood to a peak in the 20s-30s, then declining with older age. In a sample of 430 subjects aged 8-85 (Westlye et al., 2010), fractional anisotropy plateaus by the early 30s and slowly falls thereafter, while mean and radial diffusivities do the reverse (minimal in young adults, then rising in aging). Interestingly, this large study found no simple “last-in-first-out” pattern although late-maturing frontal tracts often showed pronounced aging changes, all regions eventually exhibited microstructural decline, indicating a widespread but heterogeneous aging effect rather than one specific sequence.
Regional Patterns in Aging: Many DTI studies of aging report that anterior white matter tracts (which myelinate last) are more vulnerable to aging. Salat et al. found significantly lower FA in older adults (mean age ~67) compared to young (mean ~24), especially in the frontal lobes and corpus callosum (Salat et al., 2005). In contrast, posterior tracts like the splenium of the callosum or occipital white matter showed smaller FA differences. Such findings support that age-related myelin degeneration and fiber loss are often greatest in late-developing, more complex pathways (though subsequent research has refined this view with more nuanced patterns).
Microstructural Changes with Aging: DTI metrics suggest that aging involves lower FA and increased water mobility (higher MD/apparent diffusion coefficient, ADC) (Westlye et al., 2010). In older adults, increased radial diffusivity is commonly observed, consistent with demyelination or degraded myelin packing, while axial diffusivity may also eventually decrease if axonal loss occurs. Longitudinal studies in elderly cohorts (e.g. over 60) have confirmed ongoing within-person FA declines annually. These DTI changes correlate with cognitive slowing and executive function decline in many studies, highlighting DTI’s value in tracking brain aging and its cognitive consequences.
DTI Methodological Warnings and Caveats
Single Tensor Limitations (Crossing Fibers): The basic DTI model assumes one dominant fiber orientation per voxel - an assumption often violated in the brain. In regions with crossing, kissing, or branching fibers, the tensor model yields an average that can underestimate anisotropy and obscure fiber directions. For instance, a voxel containing two crossing tracts will show an artificially low FA (appearing “isotropic”) even if each tract is highly anisotropic, and principal diffusion direction that is an average of the true tract directions. This issue can lead to misinterpretation of reduced FA: it might reflect complex fiber geometry rather than neural degeneration. Advanced high-angular-resolution methods (e.g., multi-tensor or Q-ball imaging) are recommended when crossing fibers are prevalent, or one should interpret DTI metrics in such regions with caution (Wheeler-Kingshott and Cercignani, 2009).
Axial vs. Radial Diffusivity Interpretations: While increases in radial diffusivity and decreases in axial diffusivity have been linked to demyelination and axonal injury respectively, one must be careful not to over-interpret these metrics in isolation. Wheeler-Kingshott and Cercignani showed that in voxels with multiple fiber orientations, a change in one eigenvalue can induce a “fictitious” change in another (Wheeler-Kingshott and Cercignani, 2009). For example, crossing fibers can make AD appear reduced even without axonal damage. Similarly, heavy pathology can alter the principal eigenvector direction, invalidating the simple mapping of λ1 to one fiber population. Bottom line: AD and RD are informative only in contexts where a single fiber population dominates the voxel; otherwise, observed changes might result from geometry or partial volume effects rather than specific histopathology.
Partial Volume and Free Water Contamination: DTI metrics can be skewed by mixing of tissue with free water (cerebrospinal fluid or edema). A small amount of free water in a voxel drastically lowers FA and raises diffusivity, since free water diffusion is fast and isotropic. This can mask true tissue changes - for example, a remyelinating lesion adjacent to CSF might still show low FA due to CSF contamination. Methods like the free-water elimination model address this by fitting a two-component model (Pierpaoli and Jones, 2004), effectively stripping out the isotropic diffusion component and revealing the “true” tissue tensor. Researchers should be mindful of partial voluming, especially in periventricular areas, and consider correction strategies or region-of-interest approaches to avoid artifactual findings.
Noise, Motion, and Artifacts: DTI outcomes are sensitive to data quality. Thermal noise in diffusion-weighted images leads to bias (e.g., a noise floor artificially boosts low ADC values), and insufficient signal-to-noise can make FA appear higher in low-FA regions (background noise imposes a floor) (Jones and Cercignani, 2010). Head motion is another major concern: even subtle motion can cause directional-dependent blurring or signal drop-out, which may mimic or mask true diffusion anisotropy. For example, uncorrected motion can spuriously increase FA in gray matter or produce group differences unrelated to biology (as noted in studies of populations like children or patients who move more) (Yendiki et al., 2014). Best practices include using motion correction algorithms, excluding data with excessive motion, and using robust fitting methods (e.g., iteratively reweighted least squares) that down-weight outliers caused by artifacts.
Acquisition and Analysis Choices: The choice of diffusion gradient directions and b-value can influence DTI metrics. A minimal 6-direction tensor encoding is insufficient for reliable quantitative work - more directions (20-30+) are recommended to stabilize FA/MD measures and reduce variability (Jones, 2004). Similarly, moderate b-values (~1000 s/mm²) are typically chosen to balance SNR and sensitivity; very high b-values can introduce bias in tensor-fitting, due to higher sensitivity to non-Gaussian diffusion – and require other models, such as DKI. During analysis, image alignment (registration) and smoothing can also introduce caveats: misregistration across subjects can blur tract-specific values, and heavy smoothing can artificially decrease FA in partial volume voxels. The key caveat is that DTI analyses involve many processing steps, each of which must be done carefully - otherwise, errors can propagate and lead to incorrect conclusions. Community guidelines and detailed “pitfall” checklists (e.g., (Jones and Cercignani, 2010)) are valuable resources to ensure methodological rigor in DTI studies.
References
Peter J Basser, James Mattiello, and Denis LeBihan. MR diffusion tensor spectroscopy and imaging. Biophysical Journal, 66(1):259–267, 1994. doi:10.1016/S0006-3495(94)80775-1.
Carlo Pierpaoli and Peter J Basser. Toward a quantitative assessment of diffusion anisotropy. Magnetic Resonance in Medicine, 36(6):893–906, 1996. doi:10.1002/mrm.1910360612.
Carlo Pierpaoli, Peter Jezzard, Peter J Basser, Adam Barnett, and Giovanni Di Chiro. Diffusion tensor MR imaging of the human brain. Radiology, 201(3):637–648, 1996. doi:10.1148/radiology.201.3.8939209.
Sheng-Kwei Song, Shao-Wen Sun, Matthew J. Ramsbottom, Ching Chang, James Russell, and Anne H. Cross. Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water. NeuroImage, 17(3):1429–1436, 2002. doi:10.1006/nimg.2002.1267.
Jeffrey J. Neil, Shlomo I. Shiran, Robert C. McKinstry, Gregory L. Schefft, Abraham Z. Snyder, C. Robert Almli, Ergin Akbudak, Joel A. Aronovitz, James P. Miller, Bong C. Lee, and Thomas E. Conturo. Normal brain in human newborns: apparent diffusion coefficient and diffusion anisotropy measured by using diffusion tensor MR imaging. Radiology, 209(1):57–66, 1998. doi:10.1148/radiology.209.1.9769812.
Minhui Ouyang, Jessica Dubois, Qinlin Yu, Pratik Mukherjee, and Hao Huang. Delineation of early brain development from fetuses to infants with diffusion mri and beyond. Neuroimage, 185:836–850, 2019.
Catherine Lebel and Christian Beaulieu. Longitudinal development of human brain wiring continues from childhood into adulthood. Journal of Neuroscience, 31(30):10937–10947, 2011. doi:10.1523/JNEUROSCI.5302-10.2011.
Lars T. Westlye, Kristine B. Walhovd, Anders M. Dale, Atle Bjørnerud, Paul Due-Tønnessen, Andreas Engvig, and Anders M. Fjell. Life-span changes of the human brain white matter: diffusion tensor imaging (DTI) and volumetry. Cerebral Cortex, 20(9):2055–2068, 2010. doi:10.1093/cercor/bhp280.
David H. Salat, David S. Tuch, Douglas N. Greve, Andre J. W. van der Kouwe, Nathan D. Hevelone, Anna K. Zaleta, Bruce R. Rosen, Bruce Fischl, Suzanne Corkin, H. Diana Rosas, and Anders M. Dale. Age-related alterations in white matter microstructure measured by diffusion tensor imaging. Neurobiology of Aging, 26(8):1215–1227, 2005. doi:10.1016/j.neurobiolaging.2004.09.017.
Claudia A. M. Wheeler-Kingshott and Mara Cercignani. About "axial" and "radial" diffusivities. Magnetic Resonance in Medicine, 61(5):1255–1260, 2009. doi:10.1002/mrm.21965.
C Pierpaoli and D K Jones. Removing CSF contamination in brain DT-MRIs by using a two-compartment tensor model. In Interntational Society for Magnetic Resonance in Medicine, 11, 1215. 2004.
Derek K. Jones and Mara Cercignani. Twenty-five pitfalls in the analysis of diffusion MRI data. NMR in Biomedicine, 23(7):803–820, 2010. doi:10.1002/nbm.1543.
Anastasia Yendiki, Kami Koldewyn, Sita Kakunoori, Nancy Kanwisher, and Bruce Fischl. Spurious group differences due to head motion in a diffusion MRI study. NeuroImage, 88:79–90, 2014. doi:10.1016/j.neuroimage.2013.11.027.
Derek K Jones. The effect of gradient sampling schemes on measures derived from diffusion tensor MRI: a monte carlo study. Magn. Reson. Med., 51(4):807–815, April 2004.