Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

Image-based navigation during percutaneous coronary interventions is highly challenging since it involves estimating the 3D motion of a complex topology using 2D angiographic views. A static coronary tree segmented in a pre-operative CT-scan can be overlaid on top of the angiographic frames to outline the coronary vessels, but this overlay does not account for coronary motion, which has to be mentally compensated by the cardiologist. In this paper, we propose a new approach to the motion estimation problem, where the temporal evolution of the coronary deformation over the cardiac cycle is modeled as a stochastic process. The sequence of angiographic frames is interpreted as a probabilistic evidence of the succession of unknown deformation states, which can be optimized using particle filtering. Iterative and non-rigid registration is performed in a projective manner, and relies on a feature-based similarity measure. Experiments show promising results in terms of registration accuracy, learning capability and computation time.


Journal article


Med Image Comput Comput Assist Interv

Publication Date





651 - 658


Algorithms, Angiography, Cardiology, Coronary Vessels, Diagnostic Imaging, Heart, Humans, Image Processing, Computer-Assisted, Imaging, Three-Dimensional, Likelihood Functions, Models, Statistical, Models, Theoretical, Motion, Myocardium, Percutaneous Coronary Intervention, Probability, Reproducibility of Results, Stochastic Processes, Tomography, X-Ray Computed