Modelling the Acquisition Geometry of a C-Arm Angiography System for 3D Reconstruction
CCIA '02 Proceedings of the 5th Catalonian Conference on AI: Topics in Artificial Intelligence
Automatic extraction and 3D visualization of coronary arteries from angiography sequences
BioMed'06 Proceedings of the 24th IASTED international conference on Biomedical engineering
Energy-based reconstruction of 3D curves for quality control
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Robust coronary artery tracking from fluoroscopic image sequences
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
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Stent implantation for coronary disease treatment is a highly important minimally invasive technique that avoids surgery interventions. In order to assure the success of such an intervention, it is very important to determine the real length of the lesion as exactly as possible. Currently, lesion measures are performed directly from the angiography without considering the system projective parameters or, alternatively, from the 3D reconstruction obtained from a correspondence of points defined by the physicians. In this paper, we present a method for 3D vessel reconstruction from biplane images by means of deformable models. In particular, we study the known shortcoming of point-based 3D vessel reconstruction (no intersection of projective beams) and illustrate that using snakes the reconstruction error is minimal. We validate our method by a computer-generated phantom, a real phantom and coronary vessels.