Background: Traumatic brain injury (TBI) is a major global health concern with 69 million annual cases. While neural operators have revolutionized scientific computing, existing architectures cannot handle the heterogeneous multimodal data (anatomical imaging, scalar demographics, and geometric constraints) required for patient-specific biomechanical modeling. Objective: This study introduces the first multimodal neural operator framework for biomechanics, fusing heterogeneous inputs to predict brain displacement fields for rapid TBI risk assessment. Methods: TBI modeling was reformulated as a multimodal operator learning problem. We proposed two fusion strategies: field projection for Fourier Neural Operator (FNO) architectures and branch decomposition for Deep Operator Networks (DeepONet). Four architectures (FNO, Factorized FNO, Multi-Grid FNO, and DeepONet) were extended with fusion mechanisms and evaluated on 249 in vivo Magnetic Resonance Elastography (MRE) datasets (20-90 Hz). Results: Multi-Grid FNO achieved the highest accuracy (MSE = 0.0023, 94.3% spatial fidelity). DeepONet offered the fastest inference (14.5 iterations/s, 7x speedup), suitable for edge deployment. All architectures reduced computation from hours to milliseconds. Conclusion: Multimodal neural operators enable efficient, real-time, patient-specific TBI risk assessment. This framework establishes a generalizable paradigm for heterogeneous data fusion in scientific domains, including precision medicine.

翻译：背景：创伤性脑损伤（TBI）是一个重大的全球健康问题，每年有6900万病例。尽管神经算子已经彻底改变了科学计算，但现有架构无法处理患者特异性生物力学建模所需的异质多模态数据（解剖成像、标量人口统计学数据和几何约束）。目的：本研究首次为生物力学领域引入了一个多模态神经算子框架，通过融合异质输入来预测脑位移场，以实现快速的TBI风险评估。方法：将TBI建模重新表述为一个多模态算子学习问题。我们提出了两种融合策略：针对傅里叶神经算子（FNO）架构的场投影方法，以及针对深度算子网络（DeepONet）的分支分解方法。四种架构（FNO、因子化FNO、多重网格FNO和DeepONet）通过融合机制进行了扩展，并在249个体内磁共振弹性成像（MRE）数据集（20-90 Hz）上进行了评估。结果：多重网格FNO实现了最高的精度（均方误差 = 0.0023，空间保真度94.3%）。DeepONet提供了最快的推理速度（14.5次迭代/秒，7倍加速），适合边缘部署。所有架构都将计算时间从数小时缩短至毫秒级。结论：多模态神经算子能够实现高效、实时、患者特异性的TBI风险评估。该框架为科学领域（包括精准医疗）中的异质数据融合建立了一个可推广的范式。