Title: An End-to-End AI-Based Framework for Automated Discovery of CEST/MT MR Fingerprinting Acquisition Protocols and Quantitative Deep Reconstruction (AutoCEST) Show Chinese title

Title: 基于端到端人工智能的自动发现CEST/MT磁共振指纹采集协议和定量深度重建框架（AutoCEST）

Abstract: Purpose: To develop an automated machine-learning-based method for the discovery of rapid and quantitative chemical exchange saturation transfer (CEST) MR fingerprinting acquisition and reconstruction protocols. Methods: An MR physics governed AI system was trained to generate optimized acquisition schedules and the corresponding quantitative reconstruction neural-network. The system (termed AutoCEST) is composed of a CEST saturation block, a spin dynamics module, and a deep reconstruction network, all differentiable and jointly connected. The method was validated using a variety of chemical exchange phantoms and an in-vivo mouse brain at 9.4T. Results: The acquisition times for AutoCEST optimized schedules ranged from 35-71s, with a quantitative image reconstruction time of only 29 ms. The resulting exchangeable proton concentration maps for the phantoms were in good agreement with the known solute concentrations for AutoCEST sequences (mean absolute error = 2.42 mM; Pearson's r=0.992 , p$<$0.0001), but not for an unoptimized sequence (mean absolute error = 65.19 mM; Pearson's r=-0.161, p=0.522). Similarly, improved exchange rate agreement was observed between AutoCEST and quantification of exchange using saturation power (QUESP) methods (mean absolute error: 35.8 Hz, Pearson's r=0.971, p$<$0.0001) compared to an unoptimized schedule and QUESP (mean absolute error = 58.2 Hz; Pearson's r=0.959, p$<$0.0001). The AutoCEST in-vivo mouse brain semi-solid proton volume-fractions were lower in the cortex (12.21$\pm$1.37%) compared to the white-matter (19.73 $\pm$ 3.30%), as expected, and the amide proton volume-fraction and exchange rates agreed with previous reports. Conclusion: AutoCEST can automatically generate optimized CEST/MT acquisition protocols that can be rapidly reconstructed into quantitative exchange parameter maps. Show Chinese abstract

Abstract: 目的：开发一种基于机器学习的自动化方法，用于发现快速且定量的化学交换饱和转移（CEST）磁共振指纹采集和重建协议。 方法：一个受磁共振物理定律指导的人工智能系统被训练以生成优化的采集方案和相应的定量重建神经网络。该系统（称为AutoCEST）由一个CEST饱和模块、一个自旋动力学模块和一个深度重建网络组成，所有模块均可微分且相互连接。该方法使用多种化学交换幻影和9.4T下的活体小鼠大脑进行了验证。 结果：AutoCEST优化方案的采集时间范围为35-71秒，定量图像重建时间为仅29毫秒。对于幻影的可交换质子浓度图与AutoCEST序列的已知溶质浓度具有良好一致性（平均绝对误差=2.42 mM；皮尔逊相关系数=r=0.992，p$<$0.0001），但未优化的序列则不一致（平均绝对误差=65.19 mM；皮尔逊相关系数=r=-0.161，p=0.522）。同样，与使用饱和功率进行交换量化（QUESP）方法相比，AutoCEST与QUESP方法在交换速率上表现出更好的一致性（平均绝对误差：35.8 Hz，皮尔逊相关系数=r=0.971，p$<$0.0001），而未优化的方案和QUESP则较差（平均绝对误差=58.2 Hz；皮尔逊相关系数=r=0.959，p$<$0.0001）。在活体小鼠大脑中，AutoCEST的半固体质子体积分数在皮层（12.21$\pm$1.37%）低于白质（19.73$\pm$3.30%），这符合预期，酰胺质子体积分数和交换速率与之前的研究结果一致。 结论：AutoCEST可以自动生成优化的CEST/MT采集协议，能够快速重建为定量交换参数图。