Elastic spline models for human cardiac motion estimation

Elastic splines (including dynamic "snakes" and elastic contours), minimising an energy norm of the membrane and/or thin-plate types, have been used to model many surfaces in visual reconstruction and related biomedical applications. The authors model the displacement of the material between successive cardiac images using vector splines. They define a family of elastic splines. These splines can be tuned to enforce different types and different degrees of smoothness. They assess how well these splines can be used to reconstruct human cardiac motion. The proposed method has been implemented based on MRI projection data.

[1]  Terrance E. Boult,et al.  Global models with parametric offsets as applied to cardiac motion recovery , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[2]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  John R. Kender,et al.  Visual Surface Reconstruction Using Sparse Depth Data , 1986, CVPR 1986.

[4]  Andrew Blake,et al.  Visual Reconstruction , 1987, Deep Learning for EEG-Based Brain–Computer Interfaces.

[5]  James S. Duncan,et al.  Dense nonrigid motion tracking from a sequence of velocity fields , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[6]  David Suter,et al.  Motion estimation and vector splines , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Demetri Terzopoulos,et al.  The Computation of Visible-Surface Representations , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Demetri Terzopoulos,et al.  Regularization of Inverse Visual Problems Involving Discontinuities , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Alistair A. Young,et al.  Non-rigid heart wall motion using MR tagging , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  Peter Craven,et al.  Smoothing noisy data with spline functions , 1978 .

[11]  Jinah Park,et al.  Deformable models with parameter functions: application to heart-wall modeling , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Berthold K. P. Horn Robot vision , 1986, MIT electrical engineering and computer science series.

[13]  Donald Geman,et al.  Gibbs distributions and the bayesian restoration of images , 1984 .