Abstract
In this paper, we present a method for coronary artery motion tracking in 4D cardiac CT angiogram data sets. The proposed method allows the construction of patient-specific 4D coronary motion model from pre-operative CTA which can be used for guiding totally endoscopic coronary artery bypass surgery (TECAB). The proposed approach consists of three steps: Firstly, the coronary arteries are extracted in the end-diastolic time frame using a minimal cost path approach. To achieve this, the start and end points of the coronaries are identified interactively and the minimal cost path between the start and end points is computed using A* graph search algorithm. Secondly, the cardiac motion is estimated throughout the cardiac cycle by using a non-rigid image registration technique based on a free-form B-spline transformation model and maximization of normalized mutual information. Finally, coronary arteries are tracked automatically through all other phases of the cardiac cycle. This is estimated by deforming the extracted coronaries at end-diastole to all other time frames according the motion field acquired in second step. The estimated coronary centerlines are then refined by template matching algorithm to improve the accuracy. We compare the proposed approach with two alternative approaches: The first approach is based on the minimal cost path extraction of the coronaries with start and end points manually identified in each time frame while the second approach is based on propagating the extracted coronaries from the end-diastolic time frame to other time frames using image-based non-rigid registration only. Our results show that the proposed approach performs more robustly than the non-rigid registration based method and that the resulting motion model is comparable to the motion model constructed from semi-automatic extractions of the coronaries in all time frames.
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Zhang, D.P. et al. (2010). Nonrigid Registration and Template Matching for Coronary Motion Modeling from 4D CTA. In: Fischer, B., Dawant, B.M., Lorenz, C. (eds) Biomedical Image Registration. WBIR 2010. Lecture Notes in Computer Science, vol 6204. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14366-3_19
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DOI: https://doi.org/10.1007/978-3-642-14366-3_19
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