Implications of fitting a two-compartment model in single-shell diffusion MRI

Jordan A. Chad*, Nir Sochen, J. Jean Chen, Ofer Pasternak

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

It is becoming increasingly common for studies to fit single-shell diffusion MRI data to a two-compartment model, which comprises a hindered cellular compartment and a freely diffusing isotropic compartment. These studies consistently find that the fraction of the isotropic compartment (f) is sensitive to white matter (WM) conditions and pathologies, although the actual biological source of changes in f has not been validated. In this work we put aside the biological interpretation of f and study the sensitivity implications of fitting single-shell data to a two-compartment model. We identify a nonlinear transformation between the one-compartment model (diffusion tensor imaging, DTI) and a two-compartment model in which the mean diffusivities of both compartments are effectively fixed. While the analytic relationship implies that fitting this two-compartment model does not offer any more information than DTI, it explains why metrics derived from a two-compartment model can exhibit enhanced sensitivity over DTI to certain types of WM processes, such as age-related WM differences. The sensitivity enhancement should not be viewed as a substitute for acquiring multi-shell data. Rather, the results of this study provide insight into the consequences of choosing a two-compartment model when only single-shell data is available.

Original languageEnglish
Article number215012
JournalJournal of Physics D: Applied Physics
Volume68
Issue number21
DOIs
StatePublished - 7 Nov 2023

Funding

FundersFunder number
National Institutes of HealthR01MH108574, P41EB015902
National Institutes of Health
Canadian Institutes of Health Research169688
Canadian Institutes of Health Research

    Keywords

    • brain aging
    • diffusion MRI
    • modeling
    • sensitivity
    • white matter

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