Image-based patient-specific simulation of left ventricular (LV) mechanics is valuable for understanding cardiac function and supporting clinical intervention planning, but conventional finite-element analysis (FEA) is computationally intensive. Current graph-based surrogates do not have full-cycle prediction capabilities, and physics-informed neural networks often struggle to converge on complex cardiac geometries. We present CardioGraphFENet (CGFENet), a unified graph-based surrogate for rapid full-cycle estimation of LV myocardial biomechanics, supervised by a large FEA simulation dataset. The proposed model integrates (i) a global--local graph encoder to capture mesh features with weak-form-inspired global coupling, (ii) a gated recurrent unit-based temporal encoder conditioned on the target volume-time signal to model cycle-coherent dynamics, and (iii) a cycle-consistent bidirectional formulation for both loading and inverse unloading within a single framework. These strategies enable high fidelity with respect to traditional FEA ground truths and produce physiologically plausible pressure-volume loops that match FEA results when coupled with a lumped-parameter model. In particular, the cycle-consistency strategy enables a significant reduction in FEA supervision with only minimal loss in accuracy.

翻译：基于图像的左心室力学患者特异性模拟对于理解心脏功能和支持临床干预规划具有重要价值，但传统的有限元分析计算密集。当前的基于图的替代模型缺乏全周期预测能力，而物理信息神经网络在复杂心脏几何结构上往往难以收敛。我们提出了CardioGraphFENet（CGFENet），这是一种基于图的统一替代模型，用于快速全周期估计左心室心肌生物力学，并由大型有限元模拟数据集监督。该模型整合了：（i）一种全局-局部图编码器，通过弱形式启发的全局耦合捕获网格特征；（ii）一种基于门控循环单元的时间编码器，以目标容积-时间信号为条件，用于建模周期一致动力学；（iii）一种循环一致性双向公式，可在单一框架内同时处理加载和反向卸载过程。这些策略使得模型相对于传统有限元分析基准具有高保真度，并在与集总参数模型耦合时，能生成与有限元分析结果匹配的生理学上合理的压力-容积环。特别地，循环一致性策略能够在精度损失极小的情况下，显著减少对有限元分析监督数据的依赖。