Pseudo continuous description of the diffusion MRI (dMRI) signal through multi-compartment deconvolution is a promising technique to disentangle different water pools in the brain. In this work we verified whether a deconvolution based approach with L2 regularized priors could improve the repeatability of DTI metrics computed on the brain data of 3 volunteers acquired twice. Signal fractions of free water and perfusion could reliably be quantified and removed from the diffusion signal, improving the repeatability of MD estimation both in gray and white matter.
This abstract and the presentation materials are available to members only; a login is required.