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

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...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Matei Hanu, Hesser, Jürgen, Kanschat, Guido, Moviglia, Javier, Schillings, Claudia, Stallkamp, Jan
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 11.12.2023
Subjects
Algorithms
Guide wires
Image resolution
Iterative methods
Navigation
Parameter estimation
Online AccessGet full text

Cover

More Information
Summary: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.
ISSN:2331-8422