PublicationDetail

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

K. Ralovich, M. John, E. Camus, N. Navab, T. Heimann
6DoF Catheter Detection, Application to Intracardiac Echocardiography
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2014 (bib)

Hybrid imaging systems, consisting of fluoroscopy and echocardiography, are increasingly selected for intra-operative support of minimally invasive cardiac interventions. Intracardiac Echocardiograpy (ICE) is an emerging modality with the promise of removing sedation or general anesthesia associated with Trans-esophageal Echocardiography (TEE). We introduce a novel 6 DoF? pose estimation approach for catheters (equipped with radiopaque ball markers) in single Fluoroscopy projection and investigate the method's application to a prototype ICE catheter. Machine learning based catheter detection is implemented in a Bayesian hypothesis fusion framework, followed by refinement of ball marker locations through template matching. Marker correspondence and 3D pose estimation are solved building upon POSIT. The method registers the ICE catheter and volume to the C-arm coordinate system. Experiments are performed on synthetic and porcine \textit{in-vivo} data. Target registration error (TRE), defined at the center of echo cone is the basis of our preliminary evaluation. The method reached 8.06+-7.2~mm TRE on 703 cases.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each authors copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.



Edit | Attach | Refresh | Diffs | More | Revision r1.11 - 19 Jul 2016 - 16:26 - NassirNavab

Lehrstuhl für Computer Aided Medical Procedures & Augmented Reality    rss.gif