This paper addresses the challenging task of guide wire navigation in cardiovascular interventions, focusing on the parameter estimation of a guide wire system using Ensemble Kalman Inversion (EKI) with a subsampling technique. The EKI uses an ensemble of particles to estimate the unknown quantities. However since the data misfit has to be computed for each particle in each iteration, the EKI may become computationally infeasible in the case of high-dimensional data, e.g. high-resolution images. This issue can been addressed by randomised algorithms that utilize only a random subset of the data in each iteration. We introduce and analyse a subsampling technique for the EKI, which is based on a continuous-time representation of stochastic gradient methods and apply it to on the parameter estimation of our guide wire system. Numerical experiments with real data from a simplified test setting demonstrate the potential of the method.

翻译：本文针对心血管介入手术中的导丝导航这一具有挑战性的问题，重点研究了采用集成卡尔曼反演（EKI）结合子采样技术进行导丝系统参数估计的方法。EKI利用粒子集合来估计未知量。然而，由于每次迭代中每个粒子都需要计算数据失配度，在高维数据（如高分辨率图像）情况下，EKI可能变得在计算上不可行。这一问题可通过每次迭代仅使用随机数据子集的随机化算法得到解决。我们基于随机梯度方法的连续时间表示，引入并分析了EKI的子采样技术，并将其应用于导丝系统的参数估计中。在简化测试环境下使用真实数据进行的数值实验验证了该方法的潜力。