Reduced NEXI protocol for the quantification of human gray matter microstructure on the Connectome 2.0 scanner

Biophysical diffusion MRI models such as Neurite Exchange Imaging (NEXI) are essential for probing gray matter microstructure, estimating compartment diffusivities (D_i , D_e ), neurite fraction (f), and exchange time (t_ex ). However, NEXI multi-shell, multi-diffusion-time requirements lead to prohibitively long acquisitions. Leveraging the Connectome 2.0 ultra-high gradient scanner, we developed a time-efficient protocol using an Explainable AI (XAI) framework. Combining XGBoost, SHAP, and Recursive Feature Elimination trained on synthetic signals, XAI identified an optimal 8-feature subset, halving scan time from 27 to 14 minutes. Validated in vivo in 7 healthy participants, the XAI protocol was benchmarked against the full 15-feature acquisition, a Cramér-Rao Lower Bound (CRLB) theoretical optimum, and 2 expert-designed heuristics ("Mid-Range" and "Corner"). The XAI protocol robustly reproduced parameter estimates and maintained full protocol test-retest reproducibility. Remarkably, the data-driven XAI selection converged to the theoretical CRLB optimum. This validates XAI mathematical optimality while highlighting its primary advantage: achieving gold-standard optimization without requiring complex analytical Jacobians, making it effortlessly adaptable to numerically defined models or complex noise distributions where CRLB becomes intractable. Furthermore, XAI demonstrated superior in vivo robustness compared with heuristics: "Mid-Range" sampling yielded biased t_ex estimates due to insufficient temporal diversity, while "Corner" sampling yielded unstable D_i estimates (fivefold higher coefficient of variation) due to noise sensitivity. Ultimately, this robust 14-minute NEXI protocol accelerates exchange-sensitive microstructural mapping, establishing a generalizable, model-agnostic optimization framework adaptable to future ultra-high gradient systems (e.g., Siemens Cima.X or GE MAGNUS) and existing clinical scanners.