Performance of Optical Flow tracking approaches for cardiac motion analysis

Tagging Magnetic Resonance Imaging (MRI) sequence is used for evaluating Left Ventricular contractility. In this technique, a pattern of spatially varying magnetism is applied at the end diastole. Analyzing the deformation of tag pattern during the cardiac cycle has wide applications for cardiac deformation analysis. Noninvasive myocardial tagging in MRI has shown great potential in measuring and studying the motion of the heart. This paper presents a mathematical model that simulates the real cardiac motion during myocardial tagging. We synthesized both the Spatial Modulation of Magnetization (SPAMM) and complementary Spatial Modulation of Magnetization (CSPAMM) tag patterns with arbitrary spatial frequency. Using this model, we tested the performance and limitations of different Optical Flow (OF) motion tracking techniques and compared them with the performance of Harmonic Phase (HARP) analysis technique. The results exhibit that the OF tracking accuracy differs from point to another with a noticeable over estimation at the end of systole. Also OF is performing better than HARP at the heart borders.

[1]  Leo Grady,et al.  Combining Registration and Minimum Surfaces for the Segmentation of the Left Ventricle in Cardiac Cine MR Images , 2009, MICCAI.

[2]  Jerry L. Prince,et al.  Imaging heart motion using harmonic phase MRI , 2000, IEEE Transactions on Medical Imaging.

[3]  Amir A. Amini,et al.  Coupled B-snake grids and constrained thin-plate splines for analysis of 2-D tissue deformations from tagged MRI , 1998, IEEE Transactions on Medical Imaging.

[4]  Sung Yong Shin,et al.  Scattered Data Interpolation with Multilevel B-Splines , 1997, IEEE Trans. Vis. Comput. Graph..

[5]  Mahmood Fathy,et al.  A classified and comparative study of edge detection algorithms , 2002, Proceedings. International Conference on Information Technology: Coding and Computing.

[6]  E. Zerhouni,et al.  Human heart: tagging with MR imaging--a method for noninvasive assessment of myocardial motion. , 1988, Radiology.

[7]  Li Yang,et al.  An improved motion detection method for real-time surveillance , 2008 .

[8]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[9]  Jitendra Malik,et al.  Scale-Space and Edge Detection Using Anisotropic Diffusion , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  David J. Fleet,et al.  Performance of optical flow techniques , 1994, International Journal of Computer Vision.

[11]  H. Spies,et al.  Accurate optical flow in noisy image sequences , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.