GEM: a unifying model for Gray Matter microstructure

Motivation: Gray Matter lacks a unified microstructure model, unlike White Matter. This study introduces the Generalized Exchange Model (GEM) to unify Gray Matter models, introducing exchange with simple structures. Goal(s): Evaluate GEM performance, compare it with other Gray Matter models, and assess its potential for clinical MRI. Approach: GEM parameters and equations are detailed and validated with simulated data. GEM is applied on clinical MRI data, and compared to other Gray Matter model estimates. Results: GEM successfully unifies Gray Matter diffusion models, offering plausible estimations in clinical MRI revealing microstructural patterns. Future research will optimize data acquisition and assess accuracy. Impact: The introduction of the Generalized Exchange Model holds significant implications. For scientists, it provides a unified framework, potentially simplifying and enhancing Gray Matter modeling, promoting consistency in research. Clinicians may benefit from improved specificity in diagnosing neurological conditions.