Abstract #3542

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

Alberto De Luca^1,2, 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.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.