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Published on: November 8, 2012
Multi-shell Diffusion MRI Models for White Matter Characterization in Cerebral Small Vessel Disease
Marek J Konieczny1, Anna Dewenter1, Annemieke Ter Telgte1
1From the Institute for Stroke and Dementia Research (ISD) (M.J.K., A.D., B.G., S.F., A. Kopczak, M.H., R.M., M.E., M.D.) and the Department of Radiology (O.D.), University Hospital, LMU Munich, Germany; Department of Neurology (A.t.T., K.W., A.M.T., F.-E.d.L., M.D.) and Radboud University (J.P.M., D.G.N.), Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, the Netherlands;Population Health Sciences (A.K.), German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany;Department of Neurology (R.S.), Medical University of Graz, Austria; and Munich Cluster for Systems Neurology (SyNergy) (M.D.), Germany.
Objective:
To test the hypothesis that multi-shell diffusion models improve the characterization of microstructural alterations in cerebral small vessel disease (SVD), we assessed associations with processing speed performance, longitudinal change, and reproducibility of diffusion metrics.
Methods:
We included 50 patients with sporadic and 59 patients with genetically defined SVD (cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy [CADASIL]) with cognitive testing and standardized 3T MRI, including multi-shell diffusion imaging. We applied the simple diffusion tensor imaging (DTI) model and 2 advanced models: diffusion kurtosis imaging (DKI) and neurite orientation dispersion and density imaging (NODDI). Linear regression and multivariable random forest regression (including conventional SVD markers) were used to determine associations between diffusion metrics and processing speed performance. The detection of short-term disease progression was assessed by linear mixed models in 49 patients with sporadic SVD with longitudinal high-frequency imaging (in total 459 MRIs). Intersite reproducibility was determined in 10 patients with CADASIL scanned back-to-back on 2 different 3T MRI scanners.
Results:
Metrics from DKI showed the strongest associations with processing speed performance (R 2 up to 21%) and the largest added benefit on top of conventional SVD imaging markers in patients with sporadic SVD and patients with CADASIL with lower SVD burden. Several metrics from DTI and DKI performed similarly in detecting disease progression. Reproducibility was excellent (intraclass correlation coefficient >0.93) for DTI and DKI metrics. NODDI metrics were less reproducible.
Conclusion:
Multi-shell diffusion imaging and DKI improve the detection and characterization of cognitively relevant microstructural white matter alterations in SVD. Excellent reproducibility of diffusion metrics endorses their use as SVD markers in research and clinical care. Our publicly available intersite dataset facilitates future studies.
Classification Of Evidence:
This study provides Class I evidence that in patients with SVD, diffusion MRI metrics are associated with processing speed performance.
Insights
Advanced diffusion MRI models like DKI better detect white matter changes in small vessel disease (SVD), correlating with processing speed. These diffusion metrics show excellent reproducibility for research and clinical use.
Area of Science:
- Neuroimaging
- White Matter Diseases
- Diffusion MRI
Background:
- Small vessel disease (SVD) causes microstructural white matter alterations impacting cognition.
- Current diffusion models may not fully capture these changes.
Purpose of the Study:
- To evaluate if multi-shell diffusion models enhance characterization of SVD-related microstructural changes.
- To assess associations with processing speed, disease progression, and reproducibility.
Main Methods:
- 50 sporadic SVD and 59 CADASIL patients underwent 3T MRI with multi-shell diffusion imaging.
- Diffusion Tensor Imaging (DTI), Diffusion Kurtosis Imaging (DKI), and Neurite Orientation Dispersion and Density Imaging (NODDI) models were applied.
- Associations with processing speed, longitudinal changes, and inter-site reproducibility were analyzed.
Main Results:
- DKI metrics showed strongest associations with processing speed (R² up to 21%) and improved characterization over conventional markers.
- DTI and DKI metrics demonstrated similar performance in detecting disease progression.
- DTI and DKI metrics exhibited excellent reproducibility (ICC >0.93), while NODDI metrics were less reproducible.
Conclusions:
- Multi-shell diffusion imaging, particularly DKI, improves detection and characterization of SVD-related white matter alterations.
- Diffusion MRI metrics are reproducible and suitable for SVD research and clinical applications.
- A publicly available dataset is provided to support future research.
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