4.5 Article

Microscopic anisotropy misestimation in spherical-mean single diffusion encoding MRI

Journal

MAGNETIC RESONANCE IN MEDICINE
Volume 81, Issue 5, Pages 3245-3261

Publisher

WILEY
DOI: 10.1002/mrm.27606

Keywords

double diffusion encoding; diffusion kurtosis; diffusion MRI; diffusion tensor; microscopic fractional anisotropy; single diffusion encoding; spherical mean technique

Funding

  1. H2020 European Research Council [679058]
  2. European Research Council (ERC) [679058] Funding Source: European Research Council (ERC)

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Purpose: Microscopic fractional anisotropy (mu FA) can disentangle microstructural information from orientation dispersion. While double diffusion encoding (DDE) MRI methods are widely used to extract accurate mu FA, it has only recently been proposed that powder-averaged single diffusion encoding (SDE) signals, when coupled with the diffusion standard model (SM) and a set of constraints, could be used for mu FA estimation. This study aims to evaluate mu FA as derived from the spherical mean technique (SMT) set of constraints, as well as more generally for powder-averaged SM signals. Methods: SDE experiments were performed at 16.4 T on an ex vivo mouse brain (Delta/delta = 12/1.5 ms). The mu FA maps obtained from powder-averaged SDE, signals were then compared to maps obtained from DDE-MRI experiments (Delta/tau/delta = 12/12/1.5 ms), which allow a model-free estimation of mu FA. Theory and simulations that consider different types of heterogeneity are presented for corroborating the experimental findings. Results: mu FA, as well as other estimates derived from powder-averaged SDE signals produced large deviations from the ground truth in both gray and white matter. Simulations revealed that these misestimations are likely a consequence of factors not considered by the underlying microstructural models (such as intercomponent and intracompartmental kurtosis). Conclusion: Powder-averaged SMT and (2-component) SM are unable to accurately report mu FA and other microstructural parameters in ex vivo tissues. Improper model assumptions and constraints can significantly compromise parameter specificity. Further developments and validations are required prior to implementation of these models in clinical or preclinical research.

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