Abstract
Cardiac deformation and changes therein have been linked to pathologies. Both can be extracted in detail from tagged Magnetic Resonance Imaging (tMRI) using harmonic phase (HARP) images. Although point tracking algorithms have shown to have high accuracies on HARP images, these vary with position. Detecting and discarding areas with unreliable results is crucial for use in clinical support systems. This paper assesses the capability of two confidence measures (CMs), based on energy and image structure, for detecting locations with reduced accuracy in motion tracking results. These CMs were tested on a database of simulated tMRI images containing the most common artifacts that may affect tracking accuracy. CM performance is assessed based on its capability for HARP tracking error bounding and compared in terms of significant differences detected using a multi comparison analysis of variance that takes into account the most influential factors on HARP tracking performance. Results showed that the CM based on image structure was better suited to detect unreliable optical flow vectors. In addition, it was shown that CMs can be used to detect optical flow vectors with large errors in order to improve the optical flow obtained with the HARP tracking algorithm.
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A set of volunteer sequences and phantom images are available from the 2011 STACOM challenge. For these images a set of feature points is tracked over time.
References
Zerhouni, E.A., Parish, D.M., Rogers, W.J., Yang, A., Shapiro, E.P.: 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.M., Pfeffer, M.A., Braunwald, E.: The contractile state as the major determinant in the evolution of left ventricular dysfunction in the spontaneously hypertensive rat. Circ. Res. 53, 767–778 (1983)
Götte, M.J., van Rossum, A.C., Twisk, J.W.R., Kuijer, J.P.A., Marcus, J.M., Visser, C.A.: Quantification of regional contractile function after infarction: strain analysis superior to wall thickening analysis in discriminating infarct from remote myocardium. J. Am. Coll. Cardiol. 37, 808–817 (2001)
Delhaas, T., Kotte, J., van der Toorn, A., Snoep, G., Prinzen, F.W., Arts, T.: Increase in left ventricular torsion-to-shortening ratio in children with valvular aorta stenosis. Magn. Reson. Med. 51, 135–139 (2004)
Osman, N.F., Kerwin, W.S., McVeigh, E.R., Prince, J.L.: Cardiac motion tracking using CINE harmonic phase (HARP) magnetic resonance imaging. Mag. Reson. Med. 42(6), 1048–1060 (1999)
Osman, N.F., McVeigh, E.R., Prince, J.L.: Imaging heart motion using harmonic phase MRI. IEEE Trans. Med. Imaging 19(3), 186–202 (2000)
Sampath, S., Derbyshire, J.A., Atalar, E., Osman, N.F., Prince, J.L.: Real-time imaging of two-dimensional cardiac strain using a harmonic phase magnetic resonance imaging (HARP-MRI) pulse sequence. Mag. Reson. Med. 50(1), 154–163 (2003)
Kraitchman, D.L., Sampath, S., Castillo, E., Derbyshire, J.A., Boston, R.C., Bluemke, D.A., Gerber, B.L., Prince, J.L., Osman, N.F.: Quantitative ischemia detection during cardiac magnetic resonance stress testing by use of fastharp. Circulation 107, 2025–2030 (2003)
Cheney, W., Kincaid, D.: Numerical Mathematics and Computing, 6th edn. Bob Pirtle, USA (2008)
Waks, E., Prince, J.L., Douglas, A.S.: Cardiac motion simulator for tagged MRI. In: Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA 1996) (1996). 0182
Márquez-Valle, P., Kause, H., Fuster, A., Hernández-Sabaté, A., Florack, L., Gil, D., van Assen, H.C.: Factors affecting optical flow performance in tagging magnetic resonance imaging. In: Camara, O., Mansi, T., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds.) STACOM 2014. LNCS, vol. 8896, pp. 231–238. Springer, Heidelberg (2015)
Márquez-Valle, P., Gil, D., Hernàndez-Sabaté, A.: Evaluation of the capabilities of confidence measures for assessing optical flow quality. In: International Conference on Computer Vision - Workshops (2013)
Fisher, R.: Statistical Methods and Scientific Inference. Oliver and Boyd, Edinburgh (1956)
Newbold, P., Carlson, W., Thorne, B.: Statistics for Business and Economics. Pearson Education, New York (2007)
Van Assen, H., Florack, L., Simonis, F., Westenberg, J., Strijkers, G.: Cardiac strain and rotation analysis using multi-scale optical flow. In: Wittek, A., Nielsen, P.M.F., Miller, K. (eds.) Computational Biomechanics for Medicine V, pp. 89–100. Springer, Heidelberg (2010)
Márquez-Valle, P., Gil, D., Hernàndez-Sabaté, A.: A complete confidence framework for optical flow. In: Fusiello, A., Murino, V., Cucchiara, R. (eds.) ECCV 2012 Ws/Demos, Part II. LNCS, vol. 7584, pp. 124–133. Springer, Heidelberg (2012)
Arts, T., Hunter, W., Douglas, A., Muijtjens, A., Reneman, R.: Description of the deformation of the left ventricle by a kinematic model. J. Biomech. 25(10), 1119–1127 (1992)
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)
Acknowledgements
Work supported by Spanish project TIN2012-33116. First author is supported by the Dutch Technology Foundation STW, which is part of the Netherlands Organisation for Scientic Research (NWO), and which is partly funded by the Ministry of Economic Affairs. Third author is supported by the FPI-MICINN BES-2010-031102 program. Last author is a Serra Hunter fellow.
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Kause, H. et al. (2016). Confidence Measures for Assessing the HARP Algorithm in Tagged 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 2015. Lecture Notes in Computer Science(), vol 9534. Springer, Cham. https://doi.org/10.1007/978-3-319-28712-6_8
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