Abstract
Regional wall motion and infarct scoring of MR images are routine clinical tools to grade performance and scarring in the heart. The aim of this paper is to provide a framework for automatic scoring to alert the diagnostician to potential regions of abnormality. We investigated different shape and motion configurations of a finite-element cardiac atlas of the left ventricle. Two patient populations were used: 300 asymptomatic volunteers and 105 patients with myocardial infarction, both randomly selected from the Cardiac Atlas Project database. Support vector machines were employed to estimate the boundaries between the asymptomatic control and patient groups for each of 16 standard anatomical regions in the heart. Ground truth visual wall motion scores from standard cines and infarct scoring from late enhancement were provided by experienced observers. From all configurations, end-systolic shape best predicted wall motion abnormalities (global accuracy 78%, positive predictive value 85%, specificity 91%, sensitivity 60%) and infarct scoring (74%, 72%, 91%, 44%). In conclusion, computer assisted wall motion and infarct scoring has the potential to provide robust identification of those segments requiring further clinical attention; in particular, the high specificity and relatively low sensitivity could help avoid unnecessary late gadolinium rescanning of patients.
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References
Altman, D., Bland, J.: Diagnostic tests 2: Predictive values. BMJ: British Medical Journal 309(6947), 102 (1994)
Bild, D.E., Bluemke, D.A., Burke, G.L., et al.: Multi-ethnic study of atherosclerosis: objectives and design. Am. J. Epidemiol. 156(9), 871–881 (2002)
Canty Jr., J.M., Fallavollita, J.A.: Hibernating myocardium. Journal of Nuclear Cardiology 12(1), 104–119 (2005)
Cerqueira, M.D., Weissman, N.J., Dilsizian, V., et al.: Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart. Circulation 105(4), 539–542 (2002)
Duchateau, N., De Craene, M., Piella, G., Silva, E., Doltra, A., Sitges, M., Bijnens, B., Frangi, A.: A spatiotemporal statistical atlas of motion for the quantification of abnormal myocardial tissue velocities. Medical Image Analysis (2011)
Fan, R., Chang, K., Hsieh, C., Wang, X., Lin, C.: Liblinear: A library for large linear classification. The Journal of Machine Learning Research 9, 1871–1874 (2008)
Hoffmann, R., von Bardeleben, S., Kasprzak, J.D., et al.: Analysis of regional left ventricular function by cineventriculography, cardiac magnetic resonance imaging, and unenhanced and contrast-enhanced echocardiography: A multicenter comparison of methods. J. Am. Coll. Cardiol. 47(1), 121–128 (2006)
Kadish, A.H., Bello, D., Finn, J.P., et al.: Rationale and design for the Defibrillators to Reduce Risk by Magnetic Resonance Imaging Evaluation (DETERMINE) trial. J. Cardiovasc. Electrophysiol. 20(9), 982–987 (2009)
Lekadir, K., Keenan, N.G., Pennell, D.J., Yang, G.Z.: An inter-landmark approach to 4-D shape extraction and interpretation: application to myocardial motion assessment in MRI. IEEE Trans. Med. Imaging 30(1), 52–68 (2011)
Ortiz-Pérez, J.T., Rodríguez, J., Meyers, S.N., et al.: Correspondence between the 17-segment model and coronary arterial anatomy using contrast-enhanced cardiac magnetic resonance imaging. JACC Cardiovasc. Imaging 1(3), 282–293 (2008)
Punithakumar, K., Ben Ayed, I., Ross, I.G., et al.: Detection of left ventricular motion abnormality via information measures and bayesian filtering. IEEE Trans. Inf. Technol. Biomed. 14(4), 1106–1113 (2010)
Reddy, G.P., Pujadas, S., Ordovas, K.G., Higgins, C.B.: MR imaging of ischemic heart disease. Magn. Reson. Imaging Clin. N. Am. 16(2), 201–212 (2008)
Redheuil, A.B., Kachenoura, N., Laporte, R., et al.: Interobserver variability in assessing segmental function can be reduced by combining visual analysis of CMR cine sequences with corresponding parametric images of myocardial contraction. J. Cardiovasc. Magn. Reson. 9(6), 863–872 (2007)
Suinesiaputra, A., Frangi, A.F., Kaandorp, T.A.M., et al.: Automated regional wall motion abnormality detection by combining rest and stress cardiac MRI: Correlation with contrast-enhanced MRI. J. Magn. Reson. Imaging 34(2), 270–278 (2011)
Suinesiaputra, A., Frangi, A.F., Kaandorp, T.A.M., et al.: Automated detection of regional wall motion abnormalities based on a statistical model applied to multislice short-axis cardiac MR images. IEEE Trans. Med. Imaging 28(4), 595–607 (2009)
Vapnik, V.: The nature of statistical learning theory. Springer (2000)
White, H., Norris, R., Brown, M., Brandt, P., Whitlock, R., Wild, C.: Left ventricular end-systolic volume as the major determinant of survival after recovery from myocardial infarction. Circulation 76(1), 44–51 (1987)
Young, A., Cowan, B., Thrupp, S., Hedley, W., Dell’Italia, L.: Left Ventricular Mass and Volume: Fast Calculation with Guide-Point Modeling on MR Images. Radiology 216(2), 597 (2000)
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Medrano-Gracia, P. et al. (2013). An Atlas for Cardiac MRI Regional Wall Motion and Infarct Scoring. 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 2012. Lecture Notes in Computer Science, vol 7746. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36961-2_22
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DOI: https://doi.org/10.1007/978-3-642-36961-2_22
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