We investigate the potential advantages of using distribution-valued variables to capture image information instead of the conventional techniques which use mean values to represent entire regions-of-interest. Using distribution-based distances in comparison with a mean-based approach, we explore various models to predict motor performance recovery in stroke patients. Our experiments indicate that for predictor variables Orientation Dispersion index and the Restricted Diffusion Index (diffusion scalars derived from the NODDI model), the distribution representations can lead to significantly improved regression models, over the respective mean values. Additionally, using the baseline Fugl-Meyer scores to predict follow-up Fugl-Meyer scores further enhances the model statistics.
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.
Keywords