Real-Time Guidewire Tracking for Intravascular Interventions using C-Arm Imaging Device

Real-Time Guidewire Tracking for Intravascular Interventions using C-Arm Imaging Device


Sven Lončarić

Robert Homan

Draženko Babić

Tomislav Petković

Tomislav Devčić

Vjekoslav Levačić

Hrvoje Bogunović


University of Zagreb, Faculty of Electrical Engineering and Computing (

Philips Healthcare, Best, The Netherlands (

Project duration



Philips Healthcare, Best, The Netherlands (


Visual tracking of moving objects is one of important open problems in the field of computer vision. Due to problem complexity instead of general purpose only solutions for well-defined applications are considered. Visual tracking in the field of bio-medicine is often based on transmissive or projective acquisition models making existing procedures not applicable due to incompatibility of the usual acquisition model. Better results can be achieved by utilizing methods specifically adapted to the acquisition model.

Minimally invasive endovascular interventions are good examples of surgical procedures that would be impossible without proper imaging equipment. Such interventions are the preferred treatment method for various vascular diseases. During intervention surgical instruments, such as guidewires and guiding catheters, are introduced into the vascular system and must be navigated to a point of interest, usually a pathology. The task of the medical imaging chain is to provide the surgeon with the best possible information required for successful navigation.

The aim of the project was to develop a real-time capable system that tracks the guidewire in 2D image sequence and back-projects the found guidewire into 3D space.


A prototype system that achieves processing speed of 12 fps was developed. For 2D detection stage a well-known line detection technique based on the eigenanalysis of the Hessian matrix was specifically adapted to the transmissive nature of the x-ray imaging modality. A new tracking technique that uses MAP estimation and Kalman filter to model the background and track moving objects was also developed. 3D reconstruction stage consists of two steps: (1) finding a surface that contains the guidewire, and (2) an optimization step to select one curve on the surface that is the best match under pre-specified constraints. Proposed 3D reconstruction method utilizes prior knowledge in a form of a volume that indicates positions of blood vessels and thus restricts the reconstruction. Reconstruction precision is limited by the local thickness of the vessels.


Tomislav Petković, Catheter Motion Tracking from Fluoroscopy Image Sequence, Master Thesis, Zagreb, 2006

Tomislav Petković, Visual Tracking and Reconstruction of 3D Curves in Medical Image Analysis, Doctoral Dissertation, Zagreb, 2010

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