3D catheter tip tracking in 2D X-ray image sequences using a hidden Markov model and 3D rotational angiography

Integration of pre-or peri-operative images may improve image guidance in minimally invasive interventions. In abdominal catheterization procedures such as transcatheter arterial chemoembolization, 3D pre-/peri-operative images contain relevant information, such as complete 3D vasculature, that is not directly available from 2D imaging. Accurate knowledge of the catheter tip position in 3D is currently not available, and after registration of 3D information to 2D images (angiographies), the registration is invalidated by breathing motion and thus requires continuous updates. We propose a hidden Markov model based method to track the 3D catheter position, using 2D fluoroscopic image sequences and a 3D vessel tree obtained from 3D Rotational Angiography. Such a tracking facilitates display of the catheter in the 3D anatomy, and it enables to use the 3D vessels as a roadmap in 2D imaging. The tracking is initialized with the first 2D image of the sequence. For the subsequent images, based on a state transition probability distribution and the registration observations, the catheter tip position is tracked in the 3D vessel tree using registrations to the 2D fluoroscopic images. The method is evaluated on simulated data and two clinical sequences. In the simulations, we obtain a median tip position accuracies up to 2.9 mm. On clinical sequence, the distance between the catheter and the projected vessels after registration is below 1.9 mm.

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