Assessing Cardiac Dynamics based on X-Ray Coronary Angiograms

The problem of quantitatively assessing cardiac motion including global and local dynamic performances during cardiac cycles is addressed. In vivo X-ray coronary angiographic image sequences covering several cardiac cycles are used as source image data. The three-dimensional (3-D) surface of the heart based on extended superquadrics (ESQ) surface model in each phase is constructed from 3-D coronary vessel skeletons, which are reconstructed from a pair of nearly orthogonal angiographic sequences. Complex dynamic performances of the heart are decomposed into global and local components according to a priori anatomical and dynamic knowledge that have been confirmed by medical observations and non-rigid motion theory. Parameters of all components are quantitatively estimated through motion decomposition and compensation. Consequently, cardiac dynamics during cardiac cycles are comprehensively depicted with quantitative parameters. Validation of the proposed method with clinically acquired in vivo image data has been carried out, the results of which have verified the feasibility and accuracy of the proposed method.

[1]  Weiwei Xing,et al.  Part based Structural Description Extracting and Modeling , .

[2]  Thomas S. Huang,et al.  Motion analysis and epicardial deformation estimation from angiography data , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  Thomas S. Huang,et al.  Modeling, Analysis, and Visualization of Left Ventricle Shape and Motion by Hierarchical Decomposition , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

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

[5]  Sun Jian,et al.  Sequential reconstruction of vessel skeletons from X-ray coronary angiographic sequences , 2010, Comput. Medical Imaging Graph..

[6]  Chandra Kambhamettu,et al.  Representing and recognizing complete set of geons using extended superquadrics , 2002, Object recognition supported by user interaction for service robots.

[7]  J. Garcia-Barnes,et al.  Image-based cardiac phase retrieval in intravascular ultrasound sequences , 2011, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[8]  Laurent D. Cohen,et al.  Tracking and motion analysis of the left ventricle with deformable superquadrics , 1996, Medical Image Anal..

[9]  D. E. Winch,et al.  Modelling secondary microseismic noise by normal mode summation , 2013 .

[10]  Yan Qi,et al.  Motion estimation of 3D coronary vessel skeletons from X-ray angiographic sequences , 2011, Comput. Medical Imaging Graph..

[11]  A. Tort,et al.  Gauss's law, infinite homogenous charge distributions and Helmholtz theorem , 2011 .

[12]  Marcos Martín-Fernández,et al.  Unsupervised 4D myocardium segmentation with a Markov Random Field based deformable model , 2011, Medical Image Anal..

[13]  Luc Florack,et al.  Cardiac Motion Estimation Using Covariant Derivatives and Helmholtz Decomposition , 2011, STACOM.

[14]  T Arts,et al.  Dynamics of left ventricular wall and mitral valve mechanics--a model study. , 1989, Journal of biomechanics.

[15]  M. Lee,et al.  Estimation of Local Cardiac Wall Deformation and Regional Wall Stress from Biplane Coronary Cineangiograms , 1985, IEEE Transactions on Biomedical Engineering.

[16]  Ralf W. Bauer,et al.  Accuracy of coronary artery stenosis detection with CT versus conventional coronary angiography compared with composite findings from both tests as an enhanced reference standard , 2011, European Radiology.

[17]  B. Barrientos,et al.  Measurement of transient deformation by color encoding. , 2011, Optics express.

[18]  Jinyan Fan,et al.  The modified Levenberg-Marquardt method for nonlinear equations with cubic convergence , 2012, Math. Comput..