Factors Affecting Optical Flow Performance in Tagging Magnetic Resonance Imaging

Changes in cardiac deformation patterns are correlated with cardiac pathologies. Deformation can be extracted from tagging Magnetic Resonance Imaging (tMRI) using Optical Flow (OF) techniques. For applications of OF in a clinical setting it is important to assess to what extent the performance of a particular OF method is stable across different clinical acquisition artifacts. This paper presents a statistical validation framework, based on ANOVA, to assess the motion and appearance factors that have the largest influence on OF accuracy drop. In order to validate this framework, we created a database of simulated tMRI data including the most common artifacts of MRI and test three different OF methods, including HARP.

[1]  A. Douglas,et al.  Description of the deformation of the left ventricle by a kinematic model. , 1992, Journal of biomechanics.

[2]  J. Tukey.  Comparing individual means in the analysis of variance. , 1949, Biometrics.

[3]  Alain Lalande,et al.  An Adapted Optical Flow Algorithm for Robust Quantification of Cardiac Wall Motion From Standard Cine-MR Examinations , 2012, IEEE Transactions on Information Technology in Biomedicine.

[4]  Jerry L Prince,et al.  Cardiac motion simulator for tagged MRI , 1996, Proceedings of the Workshop on Mathematical Methods in Biomedical Image Analysis.

[5]  Francesc Carreras,et al.  Variational Framework for Assessment of the Left Ventricle Motion , 2008 .

[6]  Patrick Clarysse,et al.  Mapping Displacement and Deformation of the Heart With Local Sine-Wave Modeling , 2010, IEEE Transactions on Medical Imaging.

[7]  F. Prinzen,et al.  Increase in left ventricular torsion‐to‐shortening ratio in children with valvular aortic stenosis , 2004, Magnetic resonance in medicine.

[8]  J. Kuijer,et al.  Quantification of regional contractile function after infarction: strain analysis superior to wall thickening analysis in discriminating infarct from remote myocardium. , 2001, Journal of the American College of Cardiology.

[9]  M. Pfeffer,et al.  The contractile state as the major determinant in the evolution of left ventricular dysfunction in the spontaneously hypertensive rat. , 1983, Circulation research.

[10]  Luc Florack,et al.  A Multi-scale Feature Based Optic Flow Method for 3D Cardiac Motion Estimation , 2009, SSVM.

[11]  Richard Szeliski,et al.  A Database and Evaluation Methodology for Optical Flow , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[12]  Olivier Bernard,et al.  Myocardial Motion Estimation From Medical Images Using the Monogenic Signal , 2013, IEEE Transactions on Image Processing.

[13]  Wiro J. Niessen,et al.  Reversible jump MCMC methods for fully automatic motion analysis in tagged MRI , 2012, Medical Image Anal..

[14]  L. Axel,et al.  MR imaging of motion with spatial modulation of magnetization. , 1989, Radiology.

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

[16]  Luc Florack,et al.  A New Methodology for Multiscale Myocardial Deformation and Strain Analysis Based on Tagging MRI , 2010, Int. J. Biomed. Imaging.

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

[18]  Jerry L Prince,et al.  Cardiac motion tracking using CINE harmonic phase (HARP) magnetic resonance imaging , 1999, Magnetic resonance in medicine.

[19]  Jerry L. Prince,et al.  Motion estimation from tagged MR image sequences , 1992, IEEE Trans. Medical Imaging.

[20]  Evangelos Bekiaris,et al.  Using EEG spectral components to assess algorithms for detecting fatigue , 2009, Expert Syst. Appl..

[21]  M. Gutberlet,et al.  Influence of high magnetic field strengths and parallel acquisition strategies on image quality in cardiac 2D CINE magnetic resonance imaging: comparison of 1.5 T vs. 3.0 T , 2005, European Radiology.

[22]  Andrés Santos,et al.  Motion estimation of tagged cardiac magnetic resonance images using variational techniques , 2010, Comput. Medical Imaging Graph..