超极化¹²⁹Xe与¹H联合成像可实现肺部解剖结构与通气功能可视化，为肺部疾病早期诊疗提供关键工具．但超极化¹²⁹Xe成像面临低信噪比、加速采样及多核异质性等多重挑战，现有方法存在明显局限：回归模型输出平滑，难以恢复高频细节；主流生成模型对噪声敏感，易产生伪细节且推理耗时长；缺乏针对多核异质性的模型设计，多模态互补信息未得到充分利用．为此，本文提出一种基于流匹配模型的肺部多核MRI重建框架．该方法结合回归模型与新型生成模型的优势，在定量指标与视觉细节间取得更优平衡，同时实现高效推理，提升生成模型在低信噪比数据下的鲁棒性，并提高了多核互补信息的利用效率，可为临床多核MRI加速重建提供有效技术支撑．

Hyperpolarized ¹²⁹Xe and ¹H magnetic resonance imaging (MRI) visualize lung structure and ventilation, enabling early diagnosis. However, ¹²⁹Xe imaging faces low signal noise ratio (SNR), accelerated sampling, and multinuclear heterogeneity. Existing methods have clear limits: regression models outputs are over-smoothed, losing high-frequency details; mainstream generative models are noise-sensitive, artifact-prone, and slow; multinuclear heterogeneity is unmodeled, underusing multimodal complementarity. We propose a flow-matching framework for multinuclear lung MRI reconstruction. It integrates regression and generative strengths for better metric–fidelity trade-off, efficient inference, noise robustness, and improved complementary information utilization, supporting accelerated clinical multinuclear MRI.