Abstract
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.
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References
Zerhouni, E., Parish, D., Rogers, W., et al.: Human heart: Tagging with MR imaging-a method for noninvasive assessment of myocardial motion. Radiology 169(1), 59–63 (1988)
Axel, L., Dougherty, L.: MR imaging of motion with spatial modulation of magnetization. Radiology 171(3), 841–845 (1989)
Mirsky, I., Pfeffer, J., Pfeffer, M., Braunwald, E.: The contractile state as the major determinant in the evolution of left ventricular dysfunction in the spontaneously hypertensive rat 53, 767–778 (1983)
Götte, M., van Rossum, A., Twisk, J., et al.: Quantification of regional contractile function after infarction: Strain analysis superior to wall thickening analysis in discriminating infarct from remote myocardium. JACC 37, 808–817 (2001)
Delhaas, T., Kotte, J., van der Toorn, A., et al.: Increase in left ventricular torsion-to-shortening ratio in children with valvular aorta stenosis 51, 135–139 (2004)
Horn, B., Schunck, B.: Determining optical flow. AI 17, 185–203 (1981)
Osman, N., Kerwin, W., McVeigh, E., Prince, J.: Cardiac motion tracking using CINE HARP magnetic resonance imaging 42(6), 1048–1060 (1999)
Prince, J., McVeigh, E.: Motion estimation from tagged MR image sequences 11(2), 238–249 (1992)
Florack, L., van Assen, H.: A new methodology for multiscale myocardial deformation and strain analysis based on tagging MRI (2010)
Xavier, M., Lalande, A., Walker, P., et al.: An adapted optical flow algorithm for robust quantification of cardiac wall motion from standard cine-MR examinations. Inf. Tech. in Biomed. 16(5), 859–868 (2012)
Garcia-Barnes, J., Gil, D., Pujades, S., Carreras, F.: Variational framework for assessment of the left ventricle motion. Math. Mod. of Nat. Phen. 3(6), 76–100 (2008)
Becciu, A., van Assen, H., Florack, L., et al.: A multi-scale feature based optic flow method for 3D cardiac motion estimation. In: SSVM, pp. 588–599 (2009)
Gupta, S., Prince, J.: On variable brightness optical flow for tagged MRI, pp. 323–334 (1995)
Carranza-Herrezuelo, N., Bajo, A., Sroubek, F., et al.: Motion estimation of tagged cardiac magnetic resonance images using variational techniques. Comp. Med. Im. and Graph. 34(6), 514–522 (2010)
Alessandrini, M., Basarab, A., Liebgott, H., Bernard, O.: Myocardial motion estimation from medical images using the monogenic signal. IEEE Transactions on Image Processing 22(3), 1084–1095 (2013)
Arts, T., Prinzen, F., Delhaas, T., et al.: Mapping displacement and deformation of the heart with local sine-wave modeling 29(5) (2010)
Smal, I., Carranza-Herrezuelo, N., Klein, S., et al.: Reversible jump MCMC methods for fully automatic motion analysis in tagged MRI. Medical Image Analysis 16(1), 301–324 (2012)
Arnold, S.: The theory of linear models and multivariate observations. Wiley (1997)
Jap, B., Lal, S., Fischer, P., Bekiaris, E.: Using eeg spectral components to assess algorithms for detecting fatigue. Expert Systems with Applications 36(2), 2352–2359 (2009)
Arts, T., Hunter, W., Douglas, A., et al.: Description of the deformation of the left ventricle by a kinematic model. Journal Biomechanics 25(10), 1119–1127 (1992)
Tukey, L.: Comparing individual means in the analysis of variance. Biometrics 5, 99–114 (1949)
Waks, E., Prince, J., Douglas, A.: Cardiac motion simulator for tagged MRI. In: MMBIA-Workshops, pp. 182–191 (1996)
Gutberlet, M., Schwinge, K., Freyhardt, P., et al.: Influence of high magnetic field strengths and parallel acquisition strategies on image quality in cardiac 2D CINE magnetic resonance imaging. Eur. Radiol. 15(8), 1586–1597 (2005)
Baker, S., Scharstein, D., Lewis, J., et al.: A database and evaluation methodology for optical flow. IJCV 92(1), 1–31 (2011)
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Márquez-Valle, P. et al. (2015). Factors Affecting Optical Flow Performance in Tagging Magnetic Resonance Imaging. In: Camara, O., Mansi, T., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds) Statistical Atlases and Computational Models of the Heart - Imaging and Modelling Challenges. STACOM 2014. Lecture Notes in Computer Science(), vol 8896. Springer, Cham. https://doi.org/10.1007/978-3-319-14678-2_24
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DOI: https://doi.org/10.1007/978-3-319-14678-2_24
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