用AI优化扩散MRI扫描，14分钟完成原本27分钟的脑灰质微结构检测。
Reduced NEXI protocol for the quantification of human gray matter microstructure on the Connectome 2.0 scanner
- 通过AI筛选8个关键扫描参数，大幅缩短采集时间。
- 在7名健康人中验证，参数估计精度与黄金标准相当。
- 无需复杂数学推导，适用于多种扫描设备和模型。
生物物理扩散MRI模型如神经元交换成像（NEXI）对于揭示灰质微结构至关重要，可估算细胞外/内扩散率、神经元占比及交换时间。然而，传统NEXI需多壳层、多扩散时间扫描，耗时过长。利用连接组2.0超梯度扫描仪，我们开发了一种高效协议，结合可解释人工智能（XAI）框架。基于合成信号训练的XGBoost、SHAP与递归特征消除算法，识别出最优8特征子集，将扫描时间从27分钟压缩至14分钟。在7名健康受试者中验证，该方案在参数估计精度和重测一致性上均优于完整15特征采集、理论最优的Cramér-Rao下界（CRLB）以及两种启发式策略（“中程”与“角点”）。值得注意的是，XAI选择结果收敛于CRLB最优解。这验证了其优化有效性，同时凸显核心优势：无需复杂解析雅可比矩阵，即可实现金标准优化，适用于数值模型或复杂噪声场景中难以计算CRLB的情况。此外，相比启发式方法，XAI展现出更优的体内鲁棒性：“中程”采样因时间多样性不足导致交换时间估计偏差，“角点”采样则因对噪声敏感造成胞内扩散率估计波动（变异系数达5倍）。最终，这一稳健的14分钟协议显著加速交换敏感微结构成像，建立了一种不依赖特定模型的优化框架，可适配未来超梯度系统与现有临床扫描仪。
原文摘要 · Abstract (English)
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.
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