Temporal tracking of 3D coronary arteries in projection angiograms

A method for 3D temporal tracking of a 3D coronary tree model through a sequence of biplane cineangiography images has been developed. A registration framework is formulated in which the coronary tree centerline model deforms in an external potential field defined by a multiscale analysis response map computed from the angiogram images. To constrain the procedure and to improve convergence, a set of three motion models is hierarchically used: a 3D rigid-body transformation, a 3D affine transformation, and a 3D B-spline deformation field. This 3D motion tracking approach has significant advantages over 2D methods: (1) coherent deformation of a single 3D coronary reconstruction preserves the topology of the arterial tree; (2) constraints on arterial length and regularity, which lack meaning in 2D projection space, are directly applicable in 3D; and (3) tracking arterial segments through occlusions and crossings in the projection images is simplified with knowledge of the 3D relationship of the arteries. The method has been applied to patient data and results are presented.

[1]  T. Takishima,et al.  Three-dimensional quantitative coronary angiography , 1990, IEEE Transactions on Biomedical Engineering.

[2]  R. Collorec,et al.  3D Motion and reconstruction of coronary networks , 1992, 1992 14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  Petia Radeva,et al.  3D curve reconstruction by biplane snakes , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[4]  Alok Gupta,et al.  Optimal polyline tracking for artery motion compensation in coronary angiography , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[5]  H D McIntosh,et al.  Assessment of regional myocardial performance from biplane coronary cineangiograms. , 1971, The American journal of cardiology.

[6]  Aggelos K. Katsaggelos,et al.  Motion estimation of skeletonized angiographic images using elastic registration , 1994, IEEE Trans. Medical Imaging.

[7]  Ève Coste-Manière,et al.  3D+t Modeling of Coronary Artery Tree from Standard Non Simultaneous Angiograms , 2001, MICCAI.

[8]  Elliot R. McVeigh,et al.  Four-dimensional B-spline-based motion analysis of tagged cardiac MR images , 1999, Medical Imaging.

[9]  R. Curwen,et al.  Tracking vascular motion in X-ray image sequences with Kalman snakes , 1994, Computers in Cardiology 1994.

[10]  N Guggenheim,et al.  3D determination of the intravascular volume and flow of coronary arteries. , 1994, International journal of bio-medical computing.

[11]  Andreas Wahle,et al.  Assessment of diffuse coronary artery disease by quantitative analysis of coronary morphology based upon 3-D reconstruction from biplane angiograms , 1995, IEEE Trans. Medical Imaging.

[12]  B. Smaill,et al.  Estimation of epicardial strain using the motions of coronary bifurcations in biplane cineangiography , 1992, IEEE Transactions on Biomedical Engineering.

[13]  Jean-Louis Coatrieux,et al.  Dynamic feature extraction of coronary artery motion using DSA image sequences , 1998, IEEE Transactions on Medical Imaging.

[14]  G. Mailloux,et al.  Regional epicardial dynamics computed from coronary cineangiograms , 1989, [1989] Proceedings. Computers in Cardiology.

[15]  Petia Radeva,et al.  Deformable B-Solids and Implicit Snakes for 3D Localization and Tracking of SPAMM MRI Data , 1997, Comput. Vis. Image Underst..

[16]  M. Demi,et al.  3-D heart motion from X-ray angiography , 1995, Computers in Cardiology 1995.