Multi-shell Diffusion MRI Models for White Matter Characterization in Cerebral Small Vessel Disease
Abstract
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 (R2 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.
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Information & Authors
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Copyright
© 2020 American Academy of Neurology.
Publication History
Received: December 19, 2019
Accepted: September 21, 2020
Published online: November 16, 2020
Published in print: February 2, 2021
Disclosure
A. Kopczak reports travel grants from Pfizer Pharma GmbH, unrelated to this work. M. Duering reports honoraria for lectures from Bayer Vital GmbH and Pfizer Pharma GmbH, unrelated to this work. The other authors report no disclosures. Go to Neurology.org/N for full disclosures.
Study Funding
B. Gesierich and M. Duering were supported by the German Research Foundation (DFG DU1626/1-1). S. Finsterwalder was supported by the Alzheimer Forschung Initiative e.V. (16018CB). A.M. Tuladhar was supported by the Dutch Heart Foundation (grant 2016T044) and by the Netherlands CardioVascular Research Initiative (CVON 2018-28 and 2012-06 Heart Brain Connection). A. Kopczak was supported by the European Union's Horizon 2020 research and innovation programme (grant 666881, SVDs@target). R. Schmidt was supported by the Austrian Science Fund (FWF grant I2889-B31). M. Duering was supported by the Radboud Excellence Initiative (18U.018651). Procurement of the Prisma MRI scanner in Munich was supported by the German Research Foundation (DFG INST 409/193-1 FUGG).
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