Print Email Facebook Twitter Dynamic coronary roadmapping via catheter tip tracking in X-ray fluoroscopy with deep learning based Bayesian filtering Title Dynamic coronary roadmapping via catheter tip tracking in X-ray fluoroscopy with deep learning based Bayesian filtering Author Ma, Hua (Erasmus MC) Smal, I.V. (TU Delft Optical and Laser Remote Sensing) Daemen, Joost (Erasmus MC) Walsum, Theo van (Erasmus MC) Date 2020 Abstract Percutaneous coronary intervention (PCI) is typically performed with image guidance using X-ray angiograms in which coronary arteries are opacified with X-ray opaque contrast agents. Interventional cardiologists typically navigate instruments using non-contrast-enhanced fluoroscopic images, since higher use of contrast agents increases the risk of kidney failure. When using fluoroscopic images, the interventional cardiologist needs to rely on a mental anatomical reconstruction. This paper reports on the development of a novel dynamic coronary roadmapping approach for improving visual feedback and reducing contrast use during PCI. The approach compensates cardiac and respiratory induced vessel motion by ECG alignment and catheter tip tracking in X-ray fluoroscopy, respectively. In particular, for accurate and robust tracking of the catheter tip, we proposed a new deep learning based Bayesian filtering method that integrates the detection outcome of a convolutional neural network and the motion estimation between frames using a particle filtering framework. The proposed roadmapping and tracking approaches were validated on clinical X-ray images, achieving accurate performance on both catheter tip tracking and dynamic coronary roadmapping experiments. In addition, our approach runs in real-time on a computer with a single GPU and has the potential to be integrated into the clinical workflow of PCI procedures, providing cardiologists with visual guidance during interventions without the need of extra use of contrast agent. Subject Bayesian filteringCatheter tip trackingDeep learningDynamic coronary roadmappingParticle filterX-ray fluoroscopy To reference this document use: http://resolver.tudelft.nl/uuid:063f7745-e541-41f6-b5b1-4275bc31f3fd DOI https://doi.org/10.1016/j.media.2020.101634 Embargo date 2022-01-21 ISSN 1361-8415 Source Medical Image Analysis, 61 Bibliographical note Accepted Author Manuscript Part of collection Institutional Repository Document type journal article Rights © 2020 Hua Ma, I.V. Smal, Joost Daemen, Theo van Walsum Files PDF 10.1016_j.media.2020.1016 ... script.pdf 4.91 MB Close viewer /islandora/object/uuid:063f7745-e541-41f6-b5b1-4275bc31f3fd/datastream/OBJ/view