This study explores the application of a fractional diffusion equation in diffusion-weighted magnetic resonance imaging (DW-MRI or DWI) analysis, aiming to validate and extend previous research based on an open-access dataset. A fractional-order model using the Mittag-Leffler function is implemented and validated by reproducing results presented in existing literature. The method is then applied to an open-access Connectome Diffusion Microstructure Dataset (CDMD) to analyze real brain imaging data. The computed parameter maps reveal improved contrast between white matter and gray matter, confirming the model’s potential for distinguishing tissue properties. The performance of the fractional diffusion model is compared with the conventional mono-exponential model, demonstrating improved accuracy in fitting diffusion signal attenuations in terms of root mean squared error (RMSE). This research establishes a reproducible baseline for future studies on fractional diffusion modeling in MRI and suggests expanding the study to larger datasets and exploring refinements in parameter estimation to further enhance diagnostic capabilities.
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