Multi-shell Diffusion MRI Models for White Matter Characterization in Cerebral Small Vessel Disease

Marek J. Konieczny, Anna Dewenter, Annemieke Ter Telgte, Benno Gesierich, Kim Wiegertjes, Sofia Finsterwalder, Anna Kopczak, Mathias Hübner, Rainer Malik, Anil M. Tuladhar, José P. Marques, David G. Norris, Alexandra Koch, Olaf Dietrich, Michael Ewers, Reinhold Schmidt, Frank Erik de Leeuw, Marco Duering*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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.

Original languageEnglish
Pages (from-to)e698-e708
JournalNeurology
Volume96
Issue number5
DOIs
Publication statusPublished - 2 Feb 2021
Externally publishedYes

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