用于Connectome 2.0扫描仪中人类灰质微结构量化的一种简化NEXI协议
Reduced NEXI protocol for the quantification of human gray matter microstructure on the Connectome 2.0 scanner
AI总结：
本文提出一种简化NEXI协议，利用可解释AI框架减少扫描时间，验证了其在体内的有效性，并展示了其在微结构映射中的优势。
摘要：
生物物理扩散MRI模型如神经元交换成像（NEXI）对于探测灰质微结构、估计各向同性扩散度、神经元分数和交换时间至关重要。然而，NEXI的多壳层、多扩散时间要求导致采集时间过长。利用Connectome 2.0超高速梯度扫描仪，我们开发了一种时间高效的协议，结合XGBoost、SHAP和递归特征消除算法，通过合成信号训练，识别出最优的8个特征子集，将扫描时间从27分钟减少到14分钟。在七名健康受试者体内验证后，该XAI协议与完整15特征采集、Cramér-Rao下界（CRLB）理论最优解以及两种启发式方法（
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
