论文标题
使用多室内集总参数模型和基于梯度的优化对左心室弹性的无创估计，并具有前向模式自动差异化
Non-invasive estimation of left ventricle elastance using a multi-compartment lumped parameter model and gradient-based optimization with forward-mode automatic differentiation
论文作者
论文摘要
基于非侵入性测量的左心室弹性的准确估计是在治疗瓣膜疾病期间需要的临床决策。本研究提出了一种基于心血管系统的总参数模型的参数发现方法，并结合优化和非侵入性的临床输入测量测量，以近似重要的心脏参数，包括左心室弹性。 A subset of parameters of a multi-compartment lumped parameter model was estimated using 1st order Adam gradient descent and hybrid Adam and 2nd order quasi-Newton limited-memory Broyden-Fletcher-Goldfarb-Shanno optimization routines.前向模式自动分化用于估计雅各布矩阵，并将其与常见的有限差异方法进行了比较。健康和患病心脏的合成数据作为非侵入性临床测量的代理产生，并用于评估算法。基于99％的平均左心室压力和体积的差异，选择了包括左心室弹性在内的十二个参数进行优化。与一阶优化和有限的差异方法相比，混合优化策略获得了最佳的总体结果，平均绝对百分比误差范围为6.67％至14,14％。左心室弹性估计的错误估计值和二尖瓣反流的左心室弹性估计值在包括大约2％的动脉压和瓣膜流量的合成测量值时最小，当包括体积趋势包括体积趋势时，在大约2％的情况下降至5％。但是，后者可以更好地跟踪左心室压力波形，并且在必要的设备可用时可能会被考虑。
Accurate estimates of left ventricle elastances based on non-invasive measurements are required for clinical decision-making during treatment of valvular diseases. The present study proposes a parameter discovery approach based on a lumped parameter model of the cardiovascular system in conjunction with optimization and non-invasive, clinical input measurements to approximate important cardiac parameters, including left ventricle elastances. A subset of parameters of a multi-compartment lumped parameter model was estimated using 1st order Adam gradient descent and hybrid Adam and 2nd order quasi-Newton limited-memory Broyden-Fletcher-Goldfarb-Shanno optimization routines. Forward-mode automatic differentiation was used to estimate the Jacobian matrices and compared to the common finite differences approach. Synthetic data of healthy and diseased hearts were generated as proxies for non-invasive clinical measurements and used to evaluate the algorithm. Twelve parameters including left ventricle elastances were selected for optimization based on 99% explained variation in mean left ventricle pressure and volume. The hybrid optimization strategy yielded the best overall results compared to 1st order optimization with automatic differentiation and finite difference approaches, with mean absolute percentage errors ranging from 6.67% to 14,14%. Errors in left ventricle elastance estimates for simulated aortic stenosis and mitral regurgitation were smallest when including synthetic measurements for arterial pressure and valvular flow rate at approximately 2% and degraded to roughly 5% when including volume trends as well. However, the latter resulted in better tracking of the left ventricle pressure waveforms and may be considered when the necessary equipment is available.
