{"ID":2872243,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.09513","arxiv_id":"2509.09513","title":"Reduced NEXI protocol for the quantification of human gray matter microstructure on the Connectome 2.0 scanner","abstract":"Biophysical diffusion MRI models like Neurite Exchange Imaging (NEXI) are essential for probing gray matter microstructure, estimating compartment diffusivities, neurite fraction, and exchange time. However, NEXI's multi-shell, multi-diffusion-time requirements cause 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, cutting scan time from 27 to 14 minutes. Validated in vivo in seven healthy participants, the XAI protocol was benchmarked against the full 15-feature acquisition, a Cram'er-Rao Lower Bound (CRLB) theoretical optimum, and two heuristics (\"Mid-Range\" and \"Corner\"). It robustly reproduced parameter estimates and maintained test-retest reproducibility. Remarkably, the XAI selection converged to the CRLB optimum. This validates XAI's optimality while highlighting its main advantage: achieving gold-standard optimization without complex analytical Jacobians, making it easily adaptable to numerical models or complex noise where CRLB is intractable. Furthermore, XAI showed superior in vivo robustness over heuristics: \"Mid-Range\" sampling yielded biased exchange time estimates from insufficient temporal diversity, while \"Corner\" sampling gave unstable intra-neurite diffusivity estimates (5-fold higher CV) due to noise sensitivity. Ultimately, this robust 14-minute protocol accelerates exchange-sensitive microstructural mapping, establishing a model-agnostic optimization framework adaptable to future ultra-high gradient systems and existing clinical scanners.","short_abstract":"Biophysical diffusion MRI models like Neurite Exchange Imaging (NEXI) are essential for probing gray matter microstructure, estimating compartment diffusivities, neurite fraction, and exchange time. However, NEXI's multi-shell, multi-diffusion-time requirements cause prohibitively long acquisitions. Leveraging the Conn...","url_abs":"https://arxiv.org/abs/2509.09513","url_pdf":"https://arxiv.org/pdf/2509.09513v2","authors":"[\"Quentin Uhl\",\"Tommaso Pavan\",\"Julianna Gerold\",\"Kwok-Shing Chan\",\"Yohan Jun\",\"Shohei Fujita\",\"Aneri Bhatt\",\"Yixin Ma\",\"Qiaochu Wang\",\"Hong-Hsi Lee\",\"Susie Y. Huang\",\"Berkin Bilgic\",\"Ileana Jelescu\"]","published":"2025-09-11T14:53:26Z","proceeding":"physics.med-ph","tasks":"[\"physics.med-ph\",\"cs.AI\",\"cs.CV\",\"cs.LG\",\"eess.IV\"]","methods":"[\"Diffusion Model\"]","has_code":false}