This Article Full Text (OnlineFirst PDF) Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Alert me to new issues of the journal Add to Saved Citations Download to citation manager Request Permissions Request Reprints Add to My Marked Citations Citing Articles Citing Articles via Scopus Google Scholar Articles by Koolwal, A. Articles by Liang, D. Social Bookmarking                         What's this?

An Ultrasound-Based Localization Algorithm for Catheter Ablation Guidance in the Left Atrium

¹ Department of Mechanical Engineering, Stanford University, Stanford, CA, USA
² Department of Computer Science, Stanford University, Stanford, CA, USA
³ Hansen Medical Inc, Mountain View, CA, USA
⁴ Department of Cardiovascular Medicine, Stanford School of Medicine, Stanford, CA, USA

^* To whom correspondence should be addressed. E-mail: koolwal{at}stanford.edu.

	Abstract

We present a method for catheter localization in the left atrium based on the unscented particle filter (UPF), a Monte Carlo method employed in stochastic state estimation. Using an intracardiac echo (ICE) ultrasound catheter, we acquire ultrasound images of the atrium from multiple configurations and iteratively determine the catheter's pose with respect to anatomy. At each time step, the catheter's change in pose is determined using either a six-degree-of-freedom electromagnetic pose sensor or a robotic guide catheter whose kinematics serve as a pseudo-pose measurement. Sensor and kinematic model uncertainties are explicitly considered when computing the localization estimate. Acquired ultrasound images are compared with simulated ultrasound images based on segmented computed tomography (CT) or magnetic resonance (MR) data of the left atrium. The results of these comparisons are used to refine the localization estimate. After considering less than 30 seconds' worth of ICE data, our algorithm converges to an accurate pose estimate. Furthermore, our algorithm is robust to sensor drift and kinematic model errors, as well as gradual, unmodeled movements in the anatomy. Such problems typically complicate traditional image-based localization.

First published on June 8, 2009
The International Journal of Robotics Research 2009, doi:10.1177/0278364909105332


CiteULike    Complore    Connotea    Del.icio.us    Digg    Reddit    Technorati    Twitter    What's this?