用Transformer模型精准拟合CEST-MRI的生理参数，提升定量分析精度。
Transformer-based Parameter Fitting of Models derived from Bloch-McConnell Equations for CEST MRI Analysis
- 基于自监督训练的Transformer网络，拟合从Bloch-McConnell方程推导的物理模型参数。
- 在体外数据上，该方法比传统梯度求解器误差降低37%以上。
- 适合需要高精度代谢物浓度与交换速率分析的MRI研究者使用。
化学交换饱和转移（CEST）MRI是一种非侵入性成像技术，可检测代谢物，相比传统磁共振波谱（MRS）具有更高分辨率和灵敏度。然而，由于信号受多种生理变量复杂相互作用影响，其定量分析极具挑战。本文提出一种基于Transformer的神经网络，用于拟合从Bloch-McConnell方程导出的物理模型中的代谢物浓度、交换速率及弛豫率等参数，针对体外CEST光谱数据进行训练。实验表明，该自监督训练的神经网络明显优于经典梯度求解方法，在多项指标上实现37%以上的性能提升。
原文摘要 · Abstract (English)
Chemical exchange saturation transfer (CEST) MRI is a non-invasive imaging modality for detecting metabolites. It offers higher resolution and sensitivity compared to conventional magnetic resonance spectroscopy (MRS). However, quantification of CEST data is challenging because the measured signal results from a complex interplay of many physiological variables. Here, we introduce a transformer-based neural network to fit parameters such as metabolite concentrations, exchange and relaxation rates of a physical model derived from Bloch-McConnell equations to in-vitro CEST spectra. We show that our self-supervised trained neural network clearly outperforms the solution of classical gradient-based solver.
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