A major concern in personalised models of heart mechanics is the unknown zero-pressure domain, a prerequisite for accurately predicting cardiac biomechanics. As the reference configuration cannot be captured by clinical data, studies often employ in-vivo frames which are unlikely to correspond to unloaded geometries. Alternatively, zero-pressure domain is approximated through inverse methodologies, which, however, entail assumptions pertaining to boundary conditions and material parameters. Both approaches are likely to introduce biases in estimated biomechanical properties; nevertheless, quantification of these effects is unattainable without ground-truth data. In this work, we assess the unloaded state influence on model-derived biomechanics, by employing an in-silico modelling framework relying on experimental data on porcine hearts. In-vivo images are used for model personalisation, while in-situ experiments provide a reliable approximation of the reference domain, creating a unique opportunity for a validation study. Personalised whole-cycle cardiac models are developed which employ different reference domains (image-derived, inversely estimated) and are compared against ground-truth model outcomes. Simulations are conducted with varying boundary conditions, to investigate the effect of data-derived constraints on model accuracy. Attention is given to modelling the influence of the ribcage on the epicardium, due to its close proximity to the heart in the porcine anatomy. Our results find merit in both approaches for dealing with the unknown reference domain, but also demonstrate differences in estimated biomechanical quantities such as material parameters, strains and stresses. Notably, they highlight the importance of a boundary condition accounting for the constraining influence of the ribcage, in forward and inverse biomechanical models.

心脏力学个性化模型的一个主要问题是未知的零压力域，这是准确预测心脏生物力学的先决条件。由于临床数据无法捕获参考配置，因此研究通常采用不太可能对应于空载几何形状的体内框架。或者，零压力域通过逆向方法近似，然而，这需要与边界条件和材料参数有关的假设。这两种方法都可能在估计的生物力学特性中引入偏差；然而，如果没有真实数据，这些影响的量化是无法实现的。在这项工作中，我们通过采用依赖于猪心脏实验数据的计算机建模框架来评估空载状态对模型衍生的生物力学的影响。体内图像用于模型个性化，而原位实验提供了参考域的可靠近似，为验证研究创造了独特的机会。开发了个性化的全周期心脏模型，该模型采用不同的参考域（图像派生的、反向估计的），并与真实模型结果进行比较。模拟在不同的边界条件下进行，以研究数据衍生的约束对模型准确性的影响。由于在猪解剖结构中胸腔非常靠近心脏，因此需要对胸腔对心外膜的影响进行建模。我们的结果发现两种处理未知参考域的方法都有优点，但也证明了估计的生物力学量的差异，例如材料参数，应变和压力。值得注意的是，他们强调了边界条件在正向和反向生物力学模型中解释胸腔约束影响的重要性。