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Updated: Jan 5, 2026
Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
Published on: July 28, 2013
Correction for fast pseudo-diffusive fluid motion contaminations in diffusion tensor imaging
Sonja Stieb1, Markus Klarhoefer2, Tim Finkenstaedt1
1Institute of Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich, Switzerland.
Abstract:
In this prospective study, we quantified the fast pseudo-diffusion contamination by blood perfusion or cerebrospinal fluid (CSF) intravoxel incoherent movements on the measurement of the diffusion tensor metrics in healthy brain tissue. Diffusion-weighted imaging (TR/TE = 4100 ms/90 ms; b-values: 0, 5, 10, 20, 35, 55, 80, 110, 150, 200, 300, 500, 750, 1000, 1300 s/mm2, 20 diffusion-encoding directions) was performed on a cohort of five healthy volunteers at 3 Tesla. The projections of the diffusion tensor along each diffusion-encoding direction were computed using a two b-value approach (2b), by fitting the signal to a monoexponential curve (mono), and by correcting for fast pseudo-diffusion compartments using the biexponential intravoxel incoherent motion model (IVIM) (bi). Fractional anisotropy (FA) and mean diffusivity (MD) of the diffusion tensor were quantified in regions of interest drawn over white matter areas, gray matter areas, and the ventricles. A significant dependence of the MD from the evaluation method was found in all selected regions. A lower MD was computed when accounting for the fast-diffusion compartments. A larger dependence was found in the nucleus caudatus (bi: median 0.86 10-3 mm2/s, Δ2b: -11.2%, Δmono: -14.4%; p = 0.007), in the anterior horn (bi: median 2.04 10-3 mm2/s, Δ2b: -9.4%, Δmono: -11.5%, p = 0.007) and in the posterior horn of the lateral ventricles (bi: median 2.47 10-3 mm2/s, Δ2b: -5.5%, Δmono: -11.7%; p = 0.007). Also for the FA, the signal modeling affected the computation of the anisotropy metrics. The deviation depended on the evaluated region with significant differences mainly in the nucleus caudatus (bi: median 0.15, Δ2b: +39.3%, Δmono: +14.7%; p = 0.022) and putamen (bi: median 0.19, Δ2b: +3.1%, Δmono: +17.3%; p = 0.015). Fast pseudo-diffusive regimes locally affect diffusion tensor imaging (DTI) metrics in the brain. Here, we propose the use of an IVIM-based method for correction of signal contaminations through CSF or perfusion.
Insights
Fast pseudo-diffusion contaminates diffusion tensor imaging (DTI) metrics in the brain. An intravoxel incoherent motion (IVIM) model corrects for these signal contaminations, improving accuracy in DTI measurements.
Area of Science:
- Neuroimaging
- Biophysics
- Medical Physics
Background:
- Diffusion tensor imaging (DTI) measures water diffusion in brain tissue to infer white matter structure.
- Signal contamination from blood perfusion and cerebrospinal fluid (CSF) movement can affect DTI accuracy.
- Intravoxel incoherent motion (IVIM) modeling offers a potential method to differentiate diffusion from perfusion/CSF flow.
Purpose of the Study:
- To quantify the impact of fast pseudo-diffusion (perfusion/CSF) on DTI metrics in healthy brain tissue.
- To evaluate the effectiveness of an IVIM-based model in correcting for these contaminations.
- To compare DTI metrics derived from different signal modeling approaches.
Main Methods:
- Prospective study involving five healthy volunteers at 3 Tesla.
- Diffusion-weighted imaging acquired with multiple b-values and diffusion directions.
- DTI metrics (FA, MD) computed using two b-value (2b), monoexponential (mono), and IVIM (bi) models.
- Region of interest analysis in white matter, gray matter, and ventricles.
Main Results:
- Significant differences in mean diffusivity (MD) and fractional anisotropy (FA) were observed across all evaluated regions based on the modeling method.
- The IVIM model yielded lower MD values compared to 2b and monoexponential models.
- The nucleus caudatus and lateral ventricles showed the largest MD deviations, while the nucleus caudatus and putamen showed significant FA deviations.
Conclusions:
- Fast pseudo-diffusion significantly affects DTI metric quantification in the brain.
- The proposed IVIM-based method effectively corrects for signal contaminations from CSF or perfusion.
- Accurate DTI measurements require accounting for intravoxel incoherent motion, especially in regions with high CSF or perfusion influence.
