Ensemble Kalman inversion for image guided guide wire navigation in vascular systems

作     者：Hanu, Matei Hesser, Juergen Kanschat, Guido Moviglia, Javier Schillings, Claudia Stallkamp, Jan 

作者机构：FU Berlin Berlin Germany Heidelberg Univ Mannheim Inst Intelligent Syst Med Mannheim Germany Heidelberg Univ Interdisciplinary Ctr Sci Comp Heidelberg Germany 

出 版 物：《JOURNAL OF MATHEMATICS IN INDUSTRY》 (J. Math. Ind.)

年 卷 期：2024年第14卷第1期

页      面：21页

核心收录：

学科分类：07[理学] 070104[理学-应用数学] 0701[理学-数学] 

基　　金：Berlin Mathematics Research Center MATH+ 

主　　题：Guide wire navigation Ensemble Kalman Inversion Randomized Algorithms 

摘      要：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.