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This paper addresses the challenging task of guide wire navigation incardiovascular interventions, focusing on the parameter estimation of a guidewire 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 eachiteration, the EKI may become computationally infeasible in the case ofhigh-dimensional data, e.g. high-resolution images. This issue can beenaddressed by randomised algorithms that utilize only a random subset of thedata in each iteration. We introduce and analyse a subsampling technique forthe EKI, which is based on a continuous-time representation of stochasticgradient methods and apply it to on the parameter estimation of our guide wiresystem. Numerical experiments with real data from a simplified test settingdemonstrate the potential of the method.

Ensemble Kalman Inversion for Image Guided Guide Wire Navigation in Vascular Systems