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Abstract #3542

Multi compartment deconvolution with L2 regularization and priors improves repeatability of MD estimation through free water and IVIM elimination.

Alberto De Luca1,2, Filippo Arrigoni2, Alessandra Bertoldo1, and Martijn Froeling3

1Department of Information Engineering, University of Padova, Padova, Italy, 2Neuroimaging Lab, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini (LC), Italy, 3Radiology Department, University Medical Center Utrecht, Utrecht, Netherlands

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.

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